U.S. patent application number 15/372075 was filed with the patent office on 2017-06-08 for methods for detecting and quantifying gas species analytes using differential gas species diffusion.
This patent application is currently assigned to Nanohmics, Inc.. The applicant listed for this patent is Alexander P. Greis, Kyle W. Hoover, Jeremy J. John, Chris W. Mann, Daniel R. Mitchell, Steve M. Savoy. Invention is credited to Alexander P. Greis, Kyle W. Hoover, Jeremy J. John, Chris W. Mann, Daniel R. Mitchell, Steve M. Savoy.
Application Number | 20170160221 15/372075 |
Document ID | / |
Family ID | 58800327 |
Filed Date | 2017-06-08 |
United States Patent
Application |
20170160221 |
Kind Code |
A1 |
Savoy; Steve M. ; et
al. |
June 8, 2017 |
METHODS FOR DETECTING AND QUANTIFYING GAS SPECIES ANALYTES USING
DIFFERENTIAL GAS SPECIES DIFFUSION
Abstract
Methods and sensors for the detection, identification, and
quantification of one or more gas species, including volatile
organic compounds, in a test sample are described. Methods employ
gas sensors comprising a diffusion matrix present on the sensor
surface. A gas analyte in a test sample diffuses through the matrix
and is detected upon interaction of the analyte with the sensor. A
response profile of a gas sensor to a gas analyte in the test
sample is compared to a control gas sensor response profile
determined in a similar manner for a known gas species. Comparisons
of test sample and control sample sensor response profiles enable
detection, identification, and quantification of a gas species
analyte in a test sample.
Inventors: |
Savoy; Steve M.; (Austin,
TX) ; Hoover; Kyle W.; (Austin, TX) ;
Mitchell; Daniel R.; (Austin, TX) ; John; Jeremy
J.; (Austin, TX) ; Mann; Chris W.; (Austin,
TX) ; Greis; Alexander P.; (Austin, TX) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Savoy; Steve M.
Hoover; Kyle W.
Mitchell; Daniel R.
John; Jeremy J.
Mann; Chris W.
Greis; Alexander P. |
Austin
Austin
Austin
Austin
Austin
Austin |
TX
TX
TX
TX
TX
TX |
US
US
US
US
US
US |
|
|
Assignee: |
Nanohmics, Inc.
Austin
TX
|
Family ID: |
58800327 |
Appl. No.: |
15/372075 |
Filed: |
December 7, 2016 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
62263768 |
Dec 7, 2015 |
|
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|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01N 33/0008 20130101;
G01N 33/497 20130101; G01N 33/0031 20130101; G01N 33/0047 20130101;
G01N 2033/4975 20130101; G01N 33/5308 20130101 |
International
Class: |
G01N 27/12 20060101
G01N027/12; G01N 33/497 20060101 G01N033/497; G01N 33/53 20060101
G01N033/53; G01N 33/00 20060101 G01N033/00 |
Goverment Interests
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH
[0002] The invention was made, in part, with government support
under Contract FA8650-10-C-6126 awarded by the U.S. Air Force. The
government has certain rights in the invention.
Claims
1. A method of detecting a gas species analyte in a test sample
comprising: (a) exposing a gas sensor, derivatized with a diffusion
matrix, to a control sample comprising a known gas species and
determining a control sensor response profile; (b) exposing the gas
sensor to the test sample and determining a test sample sensor
response profile; and (c) determining the presence or absence of
the gas species analyte in the test sample by comparing the test
sample sensor response profile and the control sample sensor
response profile.
2. The method of claim 1 wherein determining the test sample sensor
response profile comprises: (a) applying a bias voltage to the gas
sensor; (b) exposing the gas sensor to the test sample; (c)
stopping exposure of the gas sensor to the test sample; and (d)
determining sensor response data for a selected period of time
after applying the bias voltage to a selected period of time after
stopping exposure of the gas sensor to the test sample.
3. The method of claim 1 wherein the control sample comprises a
plurality of known gas species.
4. The method of claim 3 wherein the control sensor response
profile is stored in a database.
5. The method of claim 4 wherein comparing the test sample and
control sensor response profiles comprises using read-out
integrated circuits and implementing data analysis deconvolution
algorithms.
6. The method of claim 5 wherein the presence or absence of one or
more than one gas species analyte in the test sample is
determined.
7. The method of claim 5 wherein the concentration of one or more
than one gas species analyte in the test sample is determined.
8. The method of claim 1 wherein a plurality of control sensor
response profiles are determined with the control sample and a
plurality of test sample sensor response profiles are determined
with the test sample and wherein the plurality of test sample
sensor response profiles and the plurality of control sensor
response profiles are compared.
9. The method of claim 1 wherein comparing the test sample sensor
response profile and the control sensor response profile comprises
performing a ratiometric comparison of the profiles.
10. The method of claim 1 further comprising quantifying the gas
species analyte.
11. The method of claim 1 wherein the control sensor response
profile is determined before the test sample sensor response
profile.
12. The method of claim 1 wherein the diffusion matrix comprises
biomolecules.
13. The method of claim 12 wherein the biomolecules are coupled to
a porous support matrix.
14. The method of claim 12 wherein the biomolecules comprise one or
more than one of a nucleic acid, a protein, or a peptide.
15. The method of claim 1 wherein the test sample comprises a
liquid.
16. The method of claim 1 wherein the test sample comprises a
biological sample.
17. The method of claim 1 wherein the test sample is an
environmental sample.
18. The method of claim 1 wherein the gas sensor is in a sensor
array.
19. The method of claim 1 wherein the gas sensor is a
conductometric semiconducting metal oxide sensor.
20. The method of claim 19 wherein the gas sensor comprises a
plurality of nanotraces made by nanoimprint lithography.
21. The method of claim 20 wherein two or more nanotraces have
different widths.
22. A method of detecting the presence of one or more selected gas
species analyte in a test sample comprising: (a) exposing a gas
sensor, derivatized with a diffusion matrix, to a plurality of
control samples, each control sample comprising one or more than
one selected gas species, and determining a plurality of control
sensor response profiles; (b) exposing the gas sensor to a test
sample and determining a test sample sensor response profile; and
(c) determining the presence or absence of the one or more selected
gas species analytes in the test sample by comparing the test
sample sensor response profile and one or more than one of the
control sensor response profiles.
23. The method of claim 22 wherein the plurality of control sensor
response profiles is stored in a database and wherein comparing
comprises using read-out integrated circuits and implementing data
analysis deconvolution algorithms.
24. A method for detecting the presence of one or more selected gas
species analytes in a test sample comprising: (a) exposing an array
of gas sensors, each sensor derivatized with a selected diffusion
matrix, to a control sample comprising one or more known selected
gas species analyte, and determining a control sensor response
profile of at least one gas sensor; (b) exposing the array of
sensors to a test sample and determining a test sample sensor
response profile of the at least one gas sensor; and (c)
determining the presence or absence of the one or more than one
selected gas species analyte in the test sample by comparing the
test sample sensor response profile and the control sensor response
profile.
Description
[0001] This application claims priority under 35 U.S.C. .sctn.119
to U.S. Provisional Application No. 62/263,768 filed Dec. 7, 2015,
which is incorporated by reference herein in its entirety.
FIELD OF THE INVENTION
[0003] The invention is related to the detection and quantification
of gases including volatile organic compounds. Some applications of
the invention include the use of methods and sensors of the
invention for detection and quantification of gas species in a test
sample.
GENERAL DESCRIPTION
[0004] Detection and quantification of gases are useful in numerous
applications including for example, evaluating air samples for the
presence of hazardous gases, determining safety of air samples for
human respiration, and monitoring gas production, solvent
evaporation, reaction progress, and end-product yield during
industrial chemical production and petrochemical refining. Other
exemplary uses include characterizing human-specific volatile
organic compounds (e.g., in breath samples) for evaluating various
health factors, diagnosing conditions or diseases, determining
identity, and examining performance factors such as stress or
fatigue.
[0005] Methods and instrumentation for detection of gases have been
previously described and include mass spectrometry, optical
absorption, infrared spectroscopy and assaying with semiconducting
metal oxide sensors, to name a few. Many methods currently in use
require bulky instrumentation and vacuum chambers and may consume
large amounts of power.
[0006] Gas nanosensors that have been previously described often
lack selectivity and detect most or all gas species that bind to
the surface of the sensor. Methods for increasing specificity in
gas detection include the use of filter layers for excluding
certain gas species while permitting other gas species to interact
with a detector, the use of detectors that are specific for certain
gases, and the use of surface-attached molecules that bind specific
gas targets, wherein binding of a target gas species to the
surface-attached molecule causes a nanosensor response.
[0007] Metal oxide semiconductor type sensors, also known as
Taguchi type sensors (U.S. Pat. No. 3,695,848), are capable of
providing rapid and real time detection of gases. Fine et al.,
(2010 Sensors 10:5469-5502), Liu et al., (2012 Sensors
12(7):9635-9665), and Huang et al., (2009 Sensors 9:9903-9924)
describe fabrication and operation of Taguchi type sensors and are
incorporated by reference herein in their entirety. Absorption or
desorption of a gas on the surface of a metal oxide (e.g.,
SnO.sub.2, ZnO, TiO.sub.2, In.sub.2O.sub.3, and CuO) changes the
conductivity of the metal oxide material allowing for detection and
quantification of gas species. Metal oxide semiconductor
nanosensors and monolithic arrays of semiconducting nanosensors
fabricated on the same substrate, such as the imprinted nanotrace
nanosensors and arrays described in Savoy et al., (U.S. Patent App.
Pub. No. 2012/0178199A1 and U.S. Pat. No. 8,450,131, both of which
are incorporated by reference herein in their entirety), are useful
in embodiments of the invention and demonstrate rapid, high
sensitivity detection times for gas analytes.
[0008] Embodiments of the invention include sensors, detectors,
methods, and reagents that enable the detection, identification,
and/or quantification of different gas analyte species that are
present in a test sample. In some embodiments of the invention,
methods include determining a response profile of a gas sensor to
interaction of a gas with the sensor. A gas sensor response profile
determined with one or more known (control) gas species, is a
"control gas sensor response profile", and a gas sensor response
profile determined with a test gas sample is a "test sample sensor
response profile". A response profile of a gas sensor, or an array
of sensors, is determined for selected time periods, ranging from a
selected time before exposure of a sensor to a gas sample to a
selected time after stopping exposure of a gas sample.
[0009] In some embodiments of the invention, a gas sensor response
profile is determined with a gas sensor that is derivatized with a
diffusion matrix (i.e., the sensor is a "derivatized sensor"). In
some aspects of the invention, a diffusion matrix comprises a
molecule, a molecular compound (covalent compound), a biomolecule,
or an ionic compound that is coupled to a gas sensor surface and
that interacts non-specifically with a gas molecule during
diffusion of the gas molecule to the gas sensor. A diffusion matrix
may be coupled to a porous matrix that is on a gas sensor. In some
embodiments of the invention, a derivatized gas sensor exhibits a
response to a gas, upon interaction of the gas with the sensor,
that is different when compared with a sensor response to the gas
exhibited upon interaction of the gas with a sensor lacking a
diffusion matrix (underivatized sensor) or when compared with a
sensor response to the gas exhibited upon interaction of the gas
with a sensor derivatized with a different diffusion matrix.
Without being bound by theory, in some aspects of the invention,
molecules or compounds that are present on a sensor surface may
alter the diffusion rate of gas molecules to the sensor surface
when the sensor is exposed to a gas sample. Furthermore, these
alterations of the gas diffusion rate occur in a predictable
manner. A gas sensor response profile is affected by the diffusion
rate by Fick's law, the absorption rate of gas molecules onto the
surface of the sensor, the desorption rate of the gas molecules
from the surface of the sensor travelling through the diffusion
matrix, and the overall effect of the surface-adsorbed gas
molecules on the charge carrier concentration and mobility (i.e.,
electric current) in the gas sensor.
[0010] In embodiments of the invention, comparison of one or more
control gas sensor response profiles with one or more test sample
sensor response profiles is used to detect, identify, and/or
quantify one or more selected gas species analyte present in a test
sample. Methods of detecting a gas species analyte in a test sample
typically comprise exposing a gas sensor, derivatized with a
diffusion matrix, to a control sample comprising a known, selected
gas species analyte and determining a control sensor response
profile; exposing the gas sensor to a test sample and determining a
test sample sensor response profile; and determining the presence
or absence of the gas species analyte in the test sample by
comparing the test sample sensor response profile and the control
sample sensor response profile.
[0011] In some aspects of the invention, test sample and control
gas sensor response profiles for comparison are determined under a
variety of different conditions, such as for example, with sensors
that are derivatized with different types of diffusion matrices,
with different concentrations of gases in a gas phase or in a vapor
phase of a liquid or solid and/or with multiple different gas
species separately or combined in a single sample. In further
embodiments of the invention, detecting, identifying, and/or
quantifying a selected gas species analyte in a test sample
comprises comparing a test sample sensor response profile,
determined using a sensor having a specific selected type of
diffusion matrix, with a control sample sensor response profile
determined using the same type of sensor and diffusion matrix. In
further aspects of the invention, identification of a plurality of
selected gas species in a test sample comprises comparing a test
sample sensor response profile determined using a sensor
derivatized with a specific type of diffusion matrix with one or
more control gas sensor response profiles determined with the same
type of sensor and diffusion matrix and with the selected gas
species of interest. In additional embodiments of the invention, a
control gas sensor response profile may be determined using the
same sensor as that used for a test sample sensor response, but
prior to determining the sensor response profile with the test gas
sample. Comparisons of control sample and test sample gas sensor
response profiles determined under a variety of conditions enable,
contribute to, and enhance detection, identification, and
quantification of a gas analyte in a test sample. In additional
aspects of the invention, multiple gas sensor profiles are
determined with a test sample or with a control gas sample
comprising one or more control gas species. In further embodiments
of the invention a test sample sensor response profile or a control
sample sensor response profile is determined with an underivatized
sensor, and the response profile of the underivatized sensor is
stored in a database.
[0012] In certain embodiments of the invention multiple sensor
response profiles are determined with a single test sample or a
portion of the same test sample. As used herein, the term "test
sample" also refers to a portion of a test sample that is analyzed.
In some embodiments of the invention a test sample may comprise a
gas or a liquid, a solid, a biological sample, or an environmental
sample with a corresponding vapor component or gas phase. In
additional embodiments of the invention a test sample may be a
vapor phase or gas phase of a solid or liquid.
[0013] In some aspects of the invention, one or more control gas
sensor response profiles and test sample sensor response profiles
are stored in a database, and comparing sensor response profiles
comprises comparing one or more test sample response profiles to
one or more control gas response profiles stored in the database.
In additional aspects of the invention, deconvolution of gas sensor
response data enables the identification and quantification of
gases in a test sample.
[0014] Other embodiments of the invention are discussed throughout
this application. Embodiments described herein are understood to be
applicable to all aspects of the invention. It is contemplated that
any embodiment discussed herein can be implemented with respect to
any method or composition of the invention, and vice versa.
Furthermore, compositions of the invention can be used to achieve
methods of the invention.
[0015] The specification is most thoroughly understood in light of
the teachings and references cited within the specification. It
should be understood that the drawings, detailed description, and
specific examples, while indicating specific embodiments of the
invention, are given by way of illustration only, since various
changes and modifications within the spirit and scope of the
invention will become apparent from this detailed description to
those skilled in the art.
[0016] Any section headings used herein are for organizational
purposes only and are not to be construed as limiting the subject
matter described. All documents, or portions of documents, cited in
this application, including but not limited to patents, patent
applications, articles, books, and treatises, are hereby expressly
incorporated by reference herein in their entirety for any purpose.
To the extent documents, publications, patents, or patent
application publications incorporated by reference contradict the
invention contained in the specification, the specification will
supersede any contradictory material.
BRIEF DESCRIPTION OF THE FIGURES
[0017] The following drawings form part of the present
specification and are included to further demonstrate certain
aspects of the invention. The invention may be better understood by
reference to one or more of these drawings in combination with the
detailed description of the specification embodiments presented
herein.
[0018] FIG. 1A-FIG. 1D is a schematic depiction of an exemplary
embodiment of the invention for detecting a gas species analyte
using different diffusion matrices coupled to gas sensors present
as an array of sensors.
[0019] FIG. 2A-FIG. 2C is a schematic depiction of an exemplary
embodiment of the invention for detection of a gas species analyte
that employs different diffusion matrices (biomolecular diffusion
matrices) coupled to a porous support matrix present on gas sensors
in a sensor array.
[0020] FIG. 3A-FIG. 3C is a schematic depiction of an exemplary
embodiment of the invention for detection of gas analytes in which
different biomolecular diffusion matrices are coupled to a
freestanding porous support matrix that is then registered to
specific sensors in a sensor array.
[0021] FIG. 4A-FIG. 4D schematically illustrate the diffusion of
gas species analytes 106 (FIG. 4A) and 402 (FIG. 4C) through
different types of biomolecular diffusion matrices attached to
sensors and the resulting plotted gas sensor response profiles
(FIG. 4B, FIG. 4D) that are unique for each type of diffusion
matrix and for each gas.
[0022] FIG. 5 is a flow chart of an exemplary workflow useful in
some invention embodiments for detecting, identifying, and/or
quantifying a gas analyte in a test sample.
[0023] FIG. 6. shows multiple gas sensor response profiles
determined for each of six different volatile organic compound gas
analytes and for water, using a sensor derivatized with linker
structures.
[0024] FIG. 7A-FIG. 7D. FIG. 7A shows multiple gas sensor response
profiles, determined using a sensor derivatized with linker
structures, for each of seven different volatile organic gas
analytes from common industrial solvents. FIG. 7B shows the
averaged gas sensor response profile from all sensors for each gas.
FIG. 7C and FIG. 7D show principal component analysis plots using
data from the gas sensor response profiles.
[0025] FIG. 8A-FIG. 8C. FIG. 8A and FIG. 8B illustrate
concentration profile curves representing the concentration
distribution of a gas analyte between a gas source and a sensor
derivatized with an exemplary biomolecular diffusion matrix, during
gas exposure and diffusion of gas analytes to the sensor (FIG. 8A)
and after stopping gas exposure and during diffusion of gas
analytes from the sensor (FIG. 8B). FIG. 8C shows the corresponding
gas sensor response profile and the position in time corresponding
to each concentration profile curve. In FIG. 8A and FIG. 8B, for
ease of viewing, diffusion matrices are shown as shaded rectangles
and the gas analyte is represented as horizontal arrows.
Concentration (arb.) (y-axis), refers to concentration represented
as arbitrary units. Distances from gas source (x-axis) are in
arbitrary units.
[0026] FIG. 9. shows a one-dimensional simulation of
sensor-adsorbed concentration of a single gas analyte species with
gas sensors having different exemplary biomolecular diffusion
matrices, after a square pulse injection of a gas sample.
Concentration (arb.) refers to concentration represented as
arbitrary units. Time (x-axis) is in arbitrary units.
[0027] FIG. 10A-FIG. 10D. FIG. 10A and FIG. 10B illustrate
concentration profile curve simulations representing the
concentration distribution of a gas analyte between a gas source
and a sensor derivatized with one type of exemplary biomolecular
diffusion matrix. FIG. 10A shows concentration profile curves
during gas exposure and diffusion of gas analytes to the sensor.
FIG. 10B shows concentration profiles curves after stopping gas
exposure and during diffusion of gas analytes from the sensor. FIG.
10C and FIG. 10D illustrate concentration profile curves
representing the concentration distribution of the same gas analyte
between a gas source and a sensor derivatized with a different type
of exemplary biomolecular diffusion matrix. FIG. 10C shows
concentration profile curves during gas exposure and diffusion of
gas analytes to the sensor, and FIG. 10D shows concentration
profiles curves after stopping gas exposure and during diffusion of
gas analytes from the sensor. For ease of viewing, the biomolecular
diffusion matrices are shown as shaded rectangles and the gas
analyte is represented as horizontal arrows. Concentration (arb.)
refers to concentration represented as arbitrary units. Distances
from gas source (x-axis) are in arbitrary units.
[0028] FIG. 11 shows gas sensor response profiles determined for
exposure of gas sensors to the gas analyte N-methyl-2-pyrrolidone,
at three different gas concentrations. The sensors were
linker-derivatized or derivatized with one of four different
biomolecular diffusion matrices covalently coupled to their
surfaces. Raw current (.mu.A) was measured for determining
individual sensor responses, and data were plotted as a function of
time (seconds). Time on the x-axis represents the total elapsed
time during the recording of all sensor responses.
[0029] FIG. 12A-FIG. 12E show five series of gas sensor response
profiles in response to the gas analyte acetone, each series
determined with a gas sensor derivatized with a different type of
diffusion matrix. All y-axes represent normalized response
(R/R.sub.0), and all x-axes represent time (seconds).
[0030] FIG. 13A-FIG. 13E show five series of gas sensor response
profiles in response to the gas analyte xylene, each series
determined with a gas sensor derivatized with a different type of
diffusion matrix. All y-axes represent normalized response
(R/R.sub.0), and all x-axes represent time (seconds).
[0031] FIG. 14A-FIG. 14F show six series of gas sensor response
profiles, determined using silane-derivatized sensors in response
to six different gas analytes, each analyte at three different
concentrations of saturated gas vapor pressure. FIG. 14A, acetone;
FIG. 14B, ammonia; FIG. 14C, dimethylacetophenone; FIG. 14D,
N-methyl-2-pyrrolidone; FIG. 14E, toluene; FIG. 14F, xylene. Sensor
response profiles plotted in each of FIG. 14A-FIG. 14F represent
the average of multiple sensor response profiles determined for
each gas concentration. All y-axes represent normalized response
(R/R.sub.0), and all x-axes represent time (seconds).
[0032] FIG. 15A-FIG. 15F show six series of gas sensor response
profiles, determined using sensors derivatized with the 8-mer
peptide RVNEWVID as a diffusion matrix, in response to six
different gas analytes, each analyte at three different
concentrations of saturated gas vapor pressure. FIG. 15A, acetone;
FIG. 15B, ammonia; FIG. 15C, dimethylacetophenone; FIG. 15D,
N-methyl-2-pyrrolidone; FIG. 15E, toluene; FIG. 15F, xylene. Sensor
response profiles plotted in each of FIG. 15A-FIG. 15F represent
the average of multiple sensor response profiles determined for
each gas concentration. All y-axes represent normalized response
(R/R.sub.0), and all x-axes represent time (seconds).
[0033] FIG. 16A-FIG. 16F show six series of gas sensor response
profiles, determined using sensors derivatized with the relatively
large protein anti-biotin antibody as a diffusion matrix, in
response to six different gas analytes, each analyte at three
different concentrations of saturated gas vapor pressure. FIG. 16A,
acetone; FIG. 16B, ammonia; FIG. 16C, dimethylacetophenone; FIG.
16D, N-methyl-2-pyrrolidone; FIG. 16E, toluene; FIG. 16F, xylene.
Sensor response profiles plotted in each of FIG. 16A-FIG. 16F
represent the average of multiple sensor response profiles
determined for each gas concentration. All y-axes represent
normalized response (R/R.sub.0), and all x-axes represent time
(seconds).
[0034] FIG. 17A-FIG. 17J show ten ratiometric comparison plots of
sensor response profiles determined after exposure of sensors to
the gas analyte xylene. Sensor response profile ratios include FIG.
17A, linker-derivatized sensor to sensor derivatized with peptide
DLESFLD (1701); FIG. 17B, linker-derivatized sensor to sensor
derivatized with peptide RVNEWVID (1702); FIG. 17C,
linker-derivatized sensor to sensor derivatized with BSA (1703);
FIG. 17D, linker-derivatized sensor to sensor derivatized with
anti-biotin (1704); FIG. 17E, sensor derivatized with peptide
DLESFLD to sensor derivatized with peptide RVNEWVID (1705); FIG.
17F, sensor derivatized with peptide DLESFLD to sensor derivatized
with BSA (1706); FIG. 17G, sensor derivatized with peptide DLESFLD
to sensor derivatized with anti-biotin (1707); FIG. 17H, sensor
derivatized with peptide RVNEWVID to sensor derivatized with BSA
(1708); FIG. 17I, sensor derivatized with peptide RVNEWVID to
sensor derivatized with anti-biotin (1709); FIG. 17J, sensor
derivatized with BSA to sensor derivatized with anti-biotin (1710).
All y-axes represent normalized response (R/R.sub.0), and all
x-axes represent time (seconds).
[0035] FIG. 18A-FIG. 18J show ten ratiometric comparison plots of
sensor response profiles determined after exposure of sensors to
the gas analyte xylene. All sensors used in these examples were
underivatized bare sensors (i.e., no silane linker treatment). The
same sensors as were described in the description for FIG. 17A-FIG.
17J were exposed to xylene, but here, gas exposure to the
underivatized bare sensors occurred prior to the coupling of the
biomolecular diffusion matrices to the sensors or prior to any
silane treatment of sensors. All y-axes represent normalized
response (R/R.sub.0), and all x-axes represent time (seconds).
[0036] FIG. 19A-FIG. 19J show ten ratiometric comparison plots of
sensor response profiles determined after exposure of sensors to
the gas analyte toluene. Sensor response profile ratios include
FIG. 19A, linker-derivatized sensor to sensor derivatized with
peptide DLESFLD (1901); FIG. 19B, linker-derivatized sensor to
sensor derivatized with peptide RVNEWVID (1902); FIG. 19C,
linker-derivatized sensor to sensor derivatized with BSA (1903);
FIG. 19D, linker-derivatized sensor to sensor derivatized with
anti-biotin (1904); FIG. 19E, sensor derivatized with peptide
DLESFLD to sensor derivatized with peptide RVNEWVID (1905); FIG.
19F, sensor derivatized with peptide DLESFLD to sensor derivatized
with BSA (1906); FIG. 19G, sensor derivatized with peptide DLESFLD
to sensor derivatized with anti-biotin (1907); FIG. 19H, sensor
derivatized with peptide RVNEWVID to sensor derivatized with BSA
(1908); FIG. 19I, sensor derivatized with peptide RVNEWVID to
sensor derivatized with anti-biotin (1909); FIG. 19J, sensor
derivatized with BSA to sensor derivatized with anti-biotin (1910).
All y-axes represent normalized response (R/R.sub.0), and all
x-axes represent time (seconds).
[0037] FIG. 20A-FIG. 20J show ten ratiometric comparison plots of
sensor response profiles determined after exposure of sensors to
the gas analyte toluene. All sensors used in these examples were
underivatized bare sensors (i.e., no silane linker treatment). The
same sensors as were described in the description for FIG. 19A-FIG.
19J were exposed to toluene, but here, gas exposure to the
underivatized bare sensors occurred prior to the coupling of the
biomolecular diffusion matrices to the sensors or prior to any
silane treatment of sensors. All y-axes represent normalized
response (R/R.sub.0), and all x-axes represent time (seconds).
[0038] FIG. 21 shows five series of gas sensor response profiles
determined with the gas analyte N-methyl-2-pyrrolidone using two
different sensors derivatized with linkers 2101A, 2102A and three
different sensors derivatized with an anti-biotin biomolecular
diffusion matrix 2101D, 2102D, 2103D. Sensor response was plotted
as raw current vs. time. Time on the x-axis represents the total
elapsed time during the recording of all sensor responses using
testing regimen 501.
[0039] FIG. 22 shows a concentration dilution series of sensor
response profiles determined for exposure of linker-derivatized
sensors to methanol. The plot illustrates quantitative dependence
of sensor response profiles on gas analyte concentration. The left
side y-axis is normalized peak area, the right-side y-axis is
log-normalized peak area, and x-axis is vapor concentration
estimated by 1/2-fold dilutions (ppm).
[0040] FIG. 23A-FIG. 23B. FIG. 23A shows an exemplary embodiment of
a region of a gas analyte nanosensor fabricated using the method of
nanoimprint lithography. FIG. 23B shows an enlargement of a section
of the nanosensor having semiconducting metal oxide nanotraces with
a schematic illustration of a diffusion matrix on one nanotrace of
the sensor. Scale bar in FIG. 23A=400 nm.
[0041] FIG. 24 shows an exemplary embodiment of a 2.times.2 array
of nanosensors, each sensor comprising semiconductor metal oxide
gas detection material fabricated on CMOS read-out integrated
circuits.
[0042] FIG. 25 shows a series of gas sensor response profiles
determined using linker-derivatized semiconducting nanotrace
sensors having a width of 80 nm and spacing of 400 nm for two
different concentrations of N-methyl-2-pyrrolidone, and a sensor
response profile for one sensor having a nanotrace width of 400 nm
and spacing of 800 nm.
DESCRIPTION OF CERTAIN EXEMPLARY EMBODIMENTS
[0043] Reference will now be made in detail to certain exemplary
embodiments of the invention, some of which are illustrated in the
accompanying drawings. To assist in understanding the present
invention, certain terms are first defined. Additional definitions
are provided throughout the application.
[0044] The use of the word "a" or "an" when used in conjunction
with the term "comprising" in the claims or the specification may
mean "one," but it is also consistent with the meaning of "one or
more," "at least one," and "one or more than one." The use of the
term "at least one" in the specification and claims is meant to
include "one or more than one", and the use of the term "one or
more than one" is meant to include "at least one".
[0045] Throughout this application, the term "about" is used to
indicate that a value includes the standard deviation of error for
the device or method being employed to determine the value.
[0046] The use of the term "or" in the claims is used to mean
"and/or" unless explicitly indicated to refer to alternatives only
or the alternatives are mutually exclusive, although the disclosure
supports a definition that refers to only alternatives and
"and/or." It is also contemplated that anything listed using the
term "or" may also be specifically excluded.
[0047] As used in this specification and claim(s), the words
"comprising" (and any form of comprising, such as "comprise" and
"comprises"), "having" (and any form of having, such as "have" and
"has"), "including" (and any form of including, such as "includes"
and "include") or "containing" (and any form of containing, such as
"contains" and "contain") are inclusive or open-ended and do not
exclude additional, unrecited elements or method steps.
[0048] Disclosed herein are methods, compositions, and sensors for
the detection and quantification of a gas analyte in a test sample.
As used herein, the terms "gas analyte", "gas species", and "gas
species analyte", are used interchangeably and encompass different
"types" or "species" of gases that are present in a control sample
or a test sample. By way of example, gaseous oxygen and gaseous
nitrogen are different gas species, gas types, or gas species
analytes. The presence of one or more gas species in a test sample
may be determined, and the amounts of one or more gas species may
be quantified using methods and compositions of the invention. In
embodiments of the invention, determining the presence of a gas
analyte may include identifying the type or species of the gas
analyte. As used herein, "gas sample" may be used to refer to a gas
"test sample" or a gas "control sample". Generally, "test sample"
refers to a sample that is analyzed to determine the presence of,
identity of, and/or quantity of one or more selected gas species
analytes in the sample, and "control sample" refers to a sample
having one or more known, selected gas species analytes. In some
embodiments of the invention, the concentration of a gas analyte in
a control sample is also known. In additional embodiments of the
invention, known gas species may be added to a test gas sample.
When referring to gases in a control sample, the term "a plurality
of known gas species analytes" means that the gas sample has at
least two different types or species of known gases in the sample.
Similarly, the term "one or more known gas species analytes" means
that the sample has at least one known species or type of gas, and
the sample may have at least two different types or species of
known gases. In some embodiments of the invention, known gas
species in a control sample are selected based on the gas species
to be detected on analysis of a test sample.
[0049] The terms "sensor", "gas sensor", "nanosensor", "gas
detector", "detector" and combinations of these are used
interchangeably and refer to a gas sensor, or a gas sensor surface
that interacts with a gas molecule for detection.
[0050] As used herein, the terms "biomolecule" and "biomolecular"
refer to a molecule that is produced or capable of being produced
in or produced by a living organism. In some embodiments of the
invention, a biomolecule is an organic molecule. Exemplary organic
biomolecules include proteins, peptides; polypeptides,
oligopeptides, amino acids, polysaccharides, nucleic acids, DNA,
and RNA. Additional exemplary biomolecules include small molecule
metabolites, cytokines, hormones, lipids, antibodies, sugars,
acids, bases, and other chemical compounds. In some aspects of the
invention, biomolecules may be primary or secondary metabolites,
aptamers, or receptors. In additional aspects of the invention, a
biomolecule refers to an organic or inorganic degradation product
of a biomolecule.
[0051] Biomolecules useful in some embodiments of the invention may
be isolated from an organism or may be synthetically prepared in
vitro. In some aspects of the invention, a biomolecule is a
fragment of a cell or a cell structure, such as for example only a
region of a cell membrane, a fragment of a cell membrane, a
liposome, or a cellular organelle such as a mitochondrion, a
nucleus, a Golgi apparatus, or another subcellular structure.
Biomolecules may be purified or partially purified during or
following isolation from an organism. Numerous methods for
isolating and purifying biomolecules are known to those of skill in
the art. It is also contemplated that novel purification methods
not yet known in the art could be used for purifying biomolecules
for use in embodiments of the invention. In addition, methods for
in vitro synthesis of biological small molecules, antibodies,
peptides, nucleic acids, cell membranes, membrane mimics,
liposomes, and other biological structures are known in the
art.
[0052] The terms "diffusion matrix" and "molecular diffusion
matrix", are used interchangeably and refer to a molecule, a
molecular compound, an ionic compound, and pluralities thereof that
are coupled to a gas sensor and that interact non-specifically with
a gas molecule during diffusion of the gas molecule to the sensor
surface. In embodiments of the invention, a diffusion matrix may
modify the response profile of a gas sensor to a gas analyte as
compared to a response profile of a sensor having no diffusion
matrix or as compared to a response profile of a sensor derivatized
with a different type of diffusion matrix. The "derivatization
state" of a sensor refers to the presence or absence of a diffusion
matrix or the type of diffusion matrix on a sensor. Exemplary types
of diffusion matrices for use in some embodiments of the invention
include matrices that comprise small molecules, polymers, or
biomolecules. Different types of diffusion matrices may comprise
different molecules, different combinations of molecules, or even
different amounts of molecules.
[0053] In some embodiments of the invention, the terms
"biomolecular diffusion matrix" and "biomolecular diffusion
matrices" refer to a diffusion matrix that comprises biomolecules.
Like other types of diffusion matrices, a biomolecular diffusion
matrix present on a gas sensor surface may modify the response
profile of a gas sensor to a gas analyte as compared to a response
profile of a sensor having no biomolecular diffusion matrix or as
compared to a response profile of a sensor derivatized with a
different type of biomolecular diffusion matrix. In some
embodiments of the invention, biomolecules present in biomolecular
diffusion matrices are capable of non-specific chemical or
electrostatic interaction with at least one gas species analyte in
a gas sample. In additional embodiments of the invention, a gas
sensor may be derivatized with a biomolecular diffusion matrix
selected to have different non-specific chemical and/or
electrostatic interactions with different gas species in a gas
sample. In some aspects of the invention, a single gas sensor is
derivatized with a single selected "type" of biomolecular diffusion
matrix. The type of biomolecular diffusion matrix is determined by
the biomolecule or biomolecules that make up the diffusion matrix.
Different types of biomolecular diffusion matrices may comprise
different biomolecules, different combinations of biomolecules, or
even different amounts of biomolecules.
[0054] A diffusion matrix may be present on, bound to, or coupled
to a gas sensor. In some aspects of the invention, a diffusion
matrix is synthesized on a sensor surface. In some embodiments of
the invention, a porous support matrix that is present on, bound
to, or coupled to a sensor may be a diffusion matrix or may be
modified further to form a different type of diffusion matrix.
[0055] In some embodiments of the invention, the presence of a
diffusion matrix on a gas sensor surface alters the rate at which a
gas analyte moves from a gas source to the gas sensor surface for
detection, as compared to a sensor not having a diffusion matrix.
In additional embodiments of the invention, a diffusion matrix on a
gas sensor surface alters the rate at which a gas analyte diffuses
away from a gas sensor after interaction of the analyte with the
sensor, as compared to a sensor not having a diffusion matrix. In
some aspects of the invention, different types of diffusion
matrices may alter these rates of movement or diffusion
differently. The terms "diffusion matrix-analyte complex" and
"matrix-analyte complex" refer to any structure in which a gas
analyte and a diffusion matrix are interacting prior to adsorption
of the gas analyte to the sensor surface, or during desorption of
the gas analyte from the sensor surface after exposure of the gas
sensor to a gas sample is stopped.
[0056] Underivatized gas sensors are used in some embodiments of
the invention. When referring to gas sensors, the terms
"underivatized", "underivatized sensor", and "bare sensor" mean
that the sensor lacks a diffusion matrix.
[0057] Typically, gas detectors or gas sensors useful in
embodiments of the invention comprise structures having nanoscale
dimensions. Exemplary structures known to a person of skill in the
art include nanotubes, nanowires, nanorods, nanofibers, and
nanotraces and are referred to herein generally as
"nanostructures". Gas detectors that have nanostructures as gas
sensors are referred to herein as "nanosensors", "gas nanosensors",
or "nanodetectors". A nanostructure has at least one cross
sectional dimension, at some point along its length that is less
than about 1,000 nm (1 micron). In some embodiments of the
invention gas sensors comprise nanostructures having
cross-sectional dimensions less than about 500 nm, less than about
250 nm, less than about 100 nm, less than about 50 nm, less than
about 10 nm, or less than about 5 nm. In some aspects of the
invention, the cross-sectional dimension is from about 0.5 nm to
about 1 nm or from about 1 nm to about 5 nm. In some embodiments of
the invention, nanostructure lengths range from about 1 nm to about
100 microns, including any selected size range therebetween. Cross
sectional dimensions of nanostructures may be any size in the
ranges listed above, including the higher and lower limits listed.
All size ranges described are inclusive of the lower and upper
limit values. Size ranges within the larger ranges listed above are
also contemplated to be useful in some embodiments of the
invention. Specific size ranges may be useful in specific aspects
of the invention.
[0058] In embodiments of the invention, materials useful for gas
sensors should be amenable to the attachment of or coupling of a
diffusion matrix to the sensor surface in a manner that preserves
functionality of the matrix for transient, non-specific chemical or
electrostatic interaction with a gas species analyte in a sample.
Chemical interaction is defined as attraction or repulsion between
chemical functional groups present on the molecules in a diffusion
matrix and chemical functional groups present on a gas analyte.
Similarly, electrostatic interaction is defined as attraction or
repulsion between electrically charged groups present on the
molecules in a diffusion matrix and electrically charged groups on
the gas analyte.
[0059] Desirable materials for use in gas sensors include those
that exhibit changes in parameters such as resistance, current,
capacitance, or electrochemical potential upon interaction with a
gas. By way of example only, nanostructure gas sensors comprising
one or more of conductometric semiconductors, conducting polymers,
non-conducting polymers, carbon composites, carbon nanotubes, and
gold particles may be useful in embodiments of the invention (Savoy
et al., U.S. Pat. No. 8,450,131; Hatchett and Josowicz, Chem Rev
(2008) 108:746-769; Yoon, Nanomaterials (2013) 3:524-549.)
[0060] In some embodiments of the invention, a gas sensor may be a
conductometric semiconducting metal oxide (MO.sub.x) sensor.
Mechanisms of gas detection by semiconducting MO.sub.x gas sensors
useful in embodiments of the invention are known in the art and
have recently been reviewed (Wang et al., Sensors (2010)
10:2088-2106; Liu et al., Sensors (2012) 12:9635-9665; Huang and
Wan, Sensors (2009) 9:9903-9924; Fine et al., Sensors (2010)
10:5469-5502). Gas detection by these types of sensors is based on
the detection of a change in electrical resistance or conductance
caused by interaction of a gas molecule with the gas sensor
surface. Methods for making conductometric semiconducting MO.sub.x
gas sensors from various types of nanostructures are available to
those with skill in the art. Methods and materials for making
conductometric semiconducting MO.sub.x nanotrace sensors, using
nanoimprint lithography techniques, including materials useful as
sensor surfaces and substrates and nanosensor dimensions, are
described in Savoy et al., U.S. Pat. No. 8,450,131, which is
incorporated by reference herein in its entirety. MO.sub.x
nanotrace sensors have greater surface area-to-volume ratios
compared to thin films of thin film sensors, permitting rapid
interfacial gas exchange compared to diffusion between bulk grain
boundaries, thereby enabling the rapid collection of gas sensor
response profiles. Furthermore, high surface area-to-volume
dimensions can eliminate the need for repeated heat cycling of a
gas sensor, which can degrade calibration over time as the grain
structure changes. Temperature cycling above .about.100.degree. C.
can also degrade biomolecules. In other embodiments of the
invention, gas sensors may comprise materials other than, or in
addition to, semiconducting metal oxides. Although MO.sub.x
nanotrace sensors patterned by nanoimprint lithography have
advantages in some aspects of the invention, in other aspects of
the invention, nanostructure grain dimensions of thin film
materials may also be useful as a gas detector. In some embodiments
of the invention, methods of the invention may be implemented with
sensors that are not nanoscale-dimensioned.
[0061] In some embodiments of the invention, gas nanosensors may be
assembled into an array on a substrate. The number of nanosensors
in an array can range from one to hundreds, to thousands, to
millions, depending on the application and device parameters, such
as the number of read-out circuits and the spatial dimension of the
diffusion matrix footprint. The number of nanosensors in an array
may include any number in the range from one to millions, including
one and millions. Further embodiments may involve deposition of
sensors on the surface of CMOS read-out integrated circuits which
may comprise by way of example only, arrays of 3.times.3,
10.times.20, 40.times.60, 320.times.540, 640.times.480 VGA,
2056.times.1560 full size, 2592.times.3872 10 megapixel, and
3456.times.5184 18 megapixel. Gas sensors may be configured with
aspect ratios of 1.times.2, 1.times.3, 1.times.4, 1.times.8,
1.times.32, 1.times.100, 1.times.500, 1.times.1000, 1.times.10,000,
2.times.3, 3.times.4, and 9.times.16. Nanosensors may be grouped
together in any of a variety of numbers and array sizes and shapes.
In some embodiments of the invention, selected nanosensors in an
array can be employed as references and controls.
[0062] In embodiments of the invention, each sensor in an array may
be derivatized with a selected type of diffusion matrix, or a
sensor may be underivatized. In certain aspects of the invention,
an array of sensors comprises a plurality of nanosensors, some of
which may be derivatized with the same selected type of diffusion
matrix. In additional aspects of the invention, multiple
nanosensors derivatized with the same type of diffusion matrix may
be grouped together on a selected region of an array. In still
other aspects of the invention, an array of nanosensors may
comprise multiple groups of nanosensors, each group derivatized
with a different, selected type of diffusion matrix. In certain
aspects of the invention, one or more gas sensors may not have a
diffusion matrix, i.e., they are underivatized sensors. Exemplary
methods for arranging nanosensors and groups of nanosensors on an
array are described in Savoy et al., U.S. Pat. No. 8,450,131.
[0063] As used herein, in some embodiments of the invention, the
terms "exposing" and "exposure" and variations thereof refer to
exposing a gas sensor to a gas analyte species or to a gas sample.
Exposing a gas sensor to a gas sample comprises bringing the gas
sample in proximity to the gas sensor to allow for gas molecules to
adsorb to and desorb from the sensor, such as for example by
introducing a gas sample to a chamber in a manner that allows for
diffusion of gas molecules to a sensor surface and for interaction
of gas molecules with a diffusion matrix and with the sensor
surface. The term "exposing" encompasses "contacting". Exposing a
sensor to a gas sample encompasses contacting the sample with the
sensor and contacting the sensor with the sample.
[0064] FIG. 1A-FIG. 1D is a schematic depiction of an exemplary
embodiment of the invention for detecting a gas species analyte
using different diffusion matrices coupled to gas sensors present
as an array of sensors. In this exemplary embodiment, gas sensors
101A, 101B, 101C, 101D, present on gas sensor array 102 (FIG. 1A)
are derivatized with linker structures 103 (FIG. 1B). In this
exemplary embodiment, diffusion matrices 104B, 104C, 104D comprise
biomolecules (biomolecular diffusion matrices). Linker structures
103 facilitate attachment of the biomolecular diffusion matrices,
104B, 104C, 104D to sensors 101B, 101C, 101D respectively (FIG.
10). In some aspects of the invention, coupling of biomolecular
diffusion matrices to linkers is mediated by chemisorption, and in
additional aspects of the invention coupling is mediated by
physisorption. In some aspects of the invention, linker structures
103 may be used to facilitate the synthesis of biomolecular
diffusion matrices directly on the nanosensor surface.
Heterobifunctional linker structures useful for covalent attachment
of chemical and biological structures to surfaces are known and
commercially available (e.g., from Sigma-Aldrich Co. LLC, St.
Louis, Mo., USA). Exemplary linker structures 103 include silanes,
glutaraldehydes, succinimides, carboxylates, epoxies and
phosphonates to name only a few. In other aspects of the invention,
no biomolecular diffusion matrix is present on a sensor surface, as
illustrated by gas sensor 101A having only linker structures 103.
In these aspects of the invention, linker structures 103, lacking
biomolecules, may also function as a diffusion matrix.
[0065] In some embodiments of the invention, biomolecular diffusion
matrix 104 is synthesized in situ on the nanosensor. In some
aspects of the invention, biomolecular diffusion matrices may be
peptides 104C or nucleic acids 104D that are synthesized in situ on
the nanosensor. In certain aspects of the invention, peptides
synthesized on a nanosensor may comprise, at least 2, 3, 4, 5, 6,
7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, or 100
amino acids or any number therebetween and inclusive of the smaller
and larger sizes listed. In other aspects of the invention, peptide
length is not limited and may be any length that retains
functionality as a biomolecular diffusion matrix and that can be
synthesized on, or attached to, a nanosensor. Similarly, in certain
aspects of the invention, nucleic acids synthesized on a nanosensor
may comprise, at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30,
35, 40, 45, 50, 60, 70, 80, 90, or 100 nucleotides or any number
therebetween and inclusive of the smaller and larger sizes listed.
In other aspects of the invention, nucleic acid length is not
limited and may be any length that retains functionality as a
biomolecular diffusion matrix and that can be synthesized on, or
attached to, the surface of a nanosensor. Representative methods
for synthesizing peptides and nucleic acids on surfaces can be
found in Gao et al., (Proteomics, (2003) 3:2135-2141), and Gao et
al., (U.S. Pat. No. 6,426,184), both of which are incorporated by
reference herein in their entirety. Other synthesis methods are
known to those with skill in the art.
[0066] In other embodiments of the invention, biomolecular
diffusion matrices comprise biomolecules that are spotted or
printed onto a gas sensor. For example, peptides, proteins, nucleic
acids, or other biomolecules may be spotted or printed. Such
diffusion matrix biomolecules are typically not limited by size,
length, shape, or sequence as long as they can be spotted onto the
sensor and have some level of chemical or electrostatic interaction
with a gas analyte.
[0067] Diffusion of gas analyte 106 from gas source 105 to gas
sensor 101 is depicted in FIG. 1D. Gas flow from gas source 105 is
used to expose a sensor in a sample chamber to a gas sample having
gas species analyte 106. Gas analyte molecules diffuse directly (in
the case of linker-derivatized sensor 101A lacking a biomolecular
diffusion matrix) or through biomolecular diffusion matrices 104B,
104C, 104D to the sensors where the adsorption of gas molecules on
a sensor elicits a response by the sensor due to a change in
electrical conductance of the gas sensor (i.e., a "gas sensor
response").
[0068] FIG. 2A-FIG. 2C is a schematic depiction of an exemplary
embodiment of the invention for detection of a gas species analyte
that employs different diffusion matrices (biomolecular diffusion
matrices) coupled to a porous support matrix present on gas sensors
in a sensor array. FIG. 2A shows an exemplary embodiment of the
invention in which a porous support matrix 201, also referred to
herein as "porous matrix", is first deposited on sensor array 102.
Biomolecules are dispensed onto and/or dispersed into select
regions 202B, 202C, 202D of deposited porous support matrix 201 and
subsequently coupled to the porous matrix to form biomolecular
diffusion matrices 104B, 104C, 104D (FIG. 2B). In some embodiments
of the invention, biomolecules are dispensed to deposited porous
support matrix 201 that is present on selected sensors so that
selected types of biomolecular diffusion matrices are in
registration with selected sensors of sensor array 102. Numerous
methods may be used for dispensing, dispersing, and coupling
biomolecules to, or for directly synthesizing biomolecules on,
deposited porous support matrix 201. Some exemplary methods include
spotting, inkjet printing, drop-casting, silkscreen printing,
gravure printing, and flexographic printing. In some aspects of the
invention, biomolecules are dispensed to deposited porous support
matrix 201 using a bio-ink. In additional aspects of the invention,
a porous matrix is coupled to a sensor by absorption, for example
by chemisorption or physisorption. In some embodiments of the
invention, covalent coupling of biomolecules to porous support
matrix 201 may be employed to couple the biomolecules to structural
segments 203 of porous support matrix 201. By way of example only,
covalent coupling to structural segments 203 may be mediated by one
or more of numerous anchoring chemistries well known in the art,
such as silane heterobifunctional crosslinkers, succinimides,
glutaraldehyde, and epoxies. Other types of structures for coupling
biomolecules to structural segments 203 of deposited porous matrix
201 include silanes, peptides, nucleotides, carbohydrates, and
phosphonates to name only a few. Covalent coupling yields a
biomolecular diffusion matrix 104 comprising biomolecules
permanently attached to deposited porous support matrix 201. In
some aspects of the invention, coupling of biomolecules to a porous
support matrix may be mediated by chemisorption, such as for
example by covalent coupling, and in other aspects of the invention
coupling is mediated by physisorption.
[0069] In certain aspects of the invention, materials used for
porous support matrix 201 have a high surface area for coupling
biomolecules. In some embodiments of the invention, deposited
porous matrix 201 can be made thicker so as to increase the
thickness of biomolecular diffusion matrix 104, thereby enhancing
chemical and/or electrostatic interaction of gas analyte 106 with
the biomolecular diffusion matrix during diffusion to gas sensor
101.
[0070] In some embodiments of the invention, no biomolecules are
dispensed to deposited porous support matrix 201 over select sensor
surfaces, as in region 202A of porous support matrix 201. In the
exemplary aspect shown in FIG. 2A-C, sensor 101A is derivatized
with porous support matrix 201 in region 202A, and porous support
matrix region 202A, lacking biomolecules, may also function as a
diffusion matrix. Porous support matrix 201 may be non-biological
material, or mixtures of non-biological materials that contain an
interconnected network of volumetric space. In some aspects of the
invention, the porosity and thickness of deposited porous support
matrix 201 alter the traversal of the biomolecular diffusion matrix
by gas analyte 106 in a gas sample from source 105 prior to the
interaction of a gas analyte with sensor 101A, 101B, 101C, and 101D
(FIG. 2C) thereby affecting the gas sensor response profile.
[0071] In some aspects of the invention, structural segments 203 of
deposited porous matrix 201 comprise glass, polymer, or composite
fibers and may form a mat or sheet. Fibers may be woven or may form
a random matrix. In additional aspects of the invention, deposited
porous support matrix 201, may comprise beads.
[0072] FIG. 3A-FIG. 3C is a schematic depiction of an exemplary
embodiment of the invention for detection of gas analytes in which
different biomolecular diffusion matrices are coupled to a
freestanding porous support matrix that is then registered to
specific sensors in a sensor array. In this exemplary embodiment of
the invention, biomolecules are dispensed to freestanding porous
support matrix 301 that is not initially deposited on sensor array
102 (FIG. 3A). Dispensation of biomolecules to selected regions
302B, 302C, 302D of freestanding porous matrix 301 results in the
formation of biomolecular diffusion matrices 104B-104D (FIG. 3A).
Methods and materials described above in the discussion of FIG. 2
for dispensing, dispersing, and coupling biomolecules to deposited
porous matrix 201 are also useful with freestanding porous support
matrix 301. In some aspects of the invention, no biomolecules are
dispensed to freestanding porous support matrix 301 over select
sensor surfaces, as in region 302A of freestanding porous matrix
301.
[0073] Freestanding porous support matrix 301 comprising
biomolecules can be transferred to sensors 101 on sensor array 102
(FIG. 3B). In some embodiments, freestanding porous support matrix
301 is registered to sensor array 102 such that specific sensors
101 are derivatized with selected specific biomolecular diffusion
matrices. For example, region 302B of freestanding porous matrix
301 may receive antibody biomolecules forming biomolecular
diffusion matrix 104B, which is registered to sensor 101B.
Similarly, biomolecular diffusion matrix 104C may comprise peptides
or protein molecules with a specific conformation. Further,
biomolecular diffusion matrix 104D may comprise nucleic acid
biomolecules. Sensor array 102 can then be exposed to a gas sample
(FIG. 3C) from gas source 105 which can lead to detection of gas
species analyte 106 by sensors 101 in an analogous fashion to that
described for FIG. 1 and FIG. 2. Similarly, in this exemplary
aspect of the invention, sensor 101A is derivatized with porous
support matrix 301 in region 302A, and porous support matrix region
302A, lacking biomolecules, may also function as a diffusion
matrix.
[0074] In additional exemplary embodiments of the invention,
biomolecular diffusion matrices comprising peptides 104C or nucleic
acids 104D may be synthesized in situ on deposited porous support
matrix 201 or on freestanding porous support matrix 301. In certain
aspects of the invention, synthesized peptides may comprise, at
least 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50,
60, 70, 80, 90, or 100 amino acids or any number therebetween and
inclusive of the smaller and larger sizes listed. In other aspects
of the invention, peptide length is not limited and may be any
length that can be synthesized on a porous support matrix 201 or
301 and that retains functionality as a biomolecular diffusion
matrix. Similarly, in certain aspects of the invention, synthesized
nucleic acids may comprise, at least 2, 3, 4, 5, 6, 7, 8, 9, 10,
15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, or 100 nucleotides
or any number therebetween and inclusive of the smaller and larger
sizes listed. In other aspects of the invention, nucleic acid
length is not limited and may be any length that can be synthesized
on a porous matrix 201 or 301, and that retains functionality as a
biomolecular diffusion matrix. Representative methods for
synthesizing peptides and nucleic acids on matrix surfaces can be
found in Gao et al., (Proteomics, (2003) 3:2135-2141), and Gao et
al., (U.S. Pat. No. 6,426,184).
[0075] In embodiments of the invention, a gas sensor response
profile is determined for a selected time period, ranging from a
selected time before exposure of a sensor to a gas sample to a
selected time after stopping gas sample flow. Gas sensor response
profiles are determined by measuring a sensor parameter, such as
for example resistance, current, capacitance, or electrochemical
potential from the selected time before exposure of a sensor to a
gas sample to the selected time after stopping gas sample flow. In
embodiments of the invention, response of a gas sensor to a gas
analyte molecule requires interaction of the gas analyte molecule
with the sensor. In certain aspects of the invention, multiple gas
sensor response profiles may be determined in succession, such as
for example when recording sensor responses to multiple gas sample
exposures. In embodiments of the invention, sensor response profile
data may be represented as a plot of sensor response data, e.g.,
current or resistance vs. time. As used herein, "gas sensor
response profile" means the gas sensor response data and associated
data determined as described above. "Gas sensor response profile"
may also mean a graphical representation or other representation of
the determined gas sensor response data and associated data.
[0076] In embodiments of the invention, a gas sensor response
profile determined with a known gas species analyte (control gas
species) in a control sample is compared with a sensor profile
determined for an unknown gas species analyte in a test sample for
identifying and quantifying a gas species analyte present in a test
sample. In additional embodiments of the invention, multiple gas
sensor response profiles determined under a variety of conditions
with control and test samples are compared. Multiple unknown gas
species analytes in a test sample may be detected, identified, or
quantified. A gas sensor response profile determined with one or
more control gas species may be referred to interchangeably as a
"control gas sensor response profile", a "control sensor response
profile", or a "control sample sensor response profile". Similarly,
a gas sensor response profile determined with a test gas sample may
be referred to interchangeably as a "test sample gas sensor
response profile", a "test sample response profile", or a "test
sample sensor response profile".
[0077] FIG. 4A-FIG. 4D schematically illustrate the diffusion of
gas species analytes 106 (FIG. 4A) and 402 (FIG. 4C) through
different types of biomolecular diffusion matrices attached to
sensors and the resulting plotted gas sensor response profiles
(FIG. 4B, FIG. 4D) that are unique for each type of diffusion
matrix and for each gas. FIG. 4A-FIG. 4D demonstrate schematically
that gas sensor response profiles differ depending on the gas
species interacting with a sensor and the type of diffusion matrix
present on a sensor. FIG. 4A shows a representation of gas sensor
array 102 having a sensor 101A that is underivatized (a bare sensor
with no linkers or other molecules) and sensors 101B-101D that are
derivatized with different types of biomolecular diffusion matrices
104B-104D, respectively. Sensors are exposed to gas analyte 106
from gas source 105. Each gas sensor 101A, 101B, 101C, and 101D
produces a sensor response profile (401A, 401B, 401C, 401D) that is
the electrical response of the sensor vs. time from a selected time
before exposure of sensors to a gas sample to a selected time after
stopping gas flow from gas source 105 (FIG. 4B). The response
profiles 401B-401D determined for gas analyte 106 interacting with
sensors 101B-101D having different types of biomolecular diffusion
matrices 104 are different from one another and different from
response profile 401A determined for gas analyte 106 interacting
with underivatized bare sensor 101A.
[0078] FIG. 4C shows a representation of gas sensor array 102,
having the same respective sensors 101A, 101B, 101C, and 101D as
shown in FIG. 4A. Sensors are exposed to a different gas species
402 from gas source 403. FIG. 4D shows that the sensor response
profiles (404B, 404C, 404D) determined for gas analyte 402
interacting with sensors 101B-101D having different types of
biomolecular diffusion matrices 104 are also different from one
another and from response profile 404A determined for gas analyte
106 interacting with underivatized bare sensor 101A. Furthermore,
the sensor response profiles 401B-401D and 404B-404D determined
with the same biomolecular diffusion matrices 104B-104D but
different gas analytes (106, 402) are also different from one
another. The response of underivatized bare sensor 101A, lacking
any diffusion matrix, also differs depending on the gas species
being analyzed (401A vs. 404A). In embodiments of the invention,
such differences enable the identification and quantification of
unknown gas species analytes in a test gas sample.
[0079] Gas sensor response profiles exhibit a baseline response
prior to exposure of a sensor to a gas, followed by a response of
the sensor during gas exposure. After stopping gas analyte flow
from a gas source, gas analyte molecules may desorb and diffuse
from the surface of a sensor 101, causing the gas sensor response
profile to return to baseline. Desorption and diffusion from the
sensor surface may occur unaided as a result of a concentration
gradient or may be enhanced by introduction of a purge gas such as
clean, dry air, or conversely by evacuation. Purging may be
unassisted or may be accelerated by heating a sensor or a sensor
array 102.
[0080] The inventors found that sensor response profiles differ
depending on sensor characteristics, gas properties, or other
experimental conditions, including by way of example only, the
derivatization state of the sensor, the gas species analyte of
interest, and the type of diffusion matrix through which the
analyte diffuses on its path to a sensor. Diffusion differences can
be utilized to increase the accuracy of gas analyte detection and
identification and to aid in the quantification of one or more gas
species analytes in a test sample containing individual gas species
or gas mixtures. In some embodiments of the invention, gas sensor
response profiles 401 are determined using control (known) gas
species. In additional embodiments of the invention, gas sensor
response profiles 401 are determined using known concentrations of
control gas species. In still further embodiments of the invention,
gas sensor response profiles 401 are determined with underivatized
sensors or with sensors derivatized with selected types and
configurations of diffusion matrices. One or more gas sensor
profiles determined under different experimental conditions, with
control gas species, may be stored in a database. Comparisons of
these stored gas sensor response profiles with gas sensor response
profiles determined for a test sample can be used to detect,
identify, and/or quantify one or more gas species analyte in a test
sample. In some aspects of the invention, a test sample or control
sample or a portion of a test or control sample may be diluted
prior to determining a gas sensor response profile.
[0081] FIG. 5 is a flow chart of an exemplary workflow useful in
some embodiments of the invention for detecting, identifying,
and/or quantifying a gas analyte in a test sample. In some
embodiments of the invention, evaluating a test sample for the
presence of a gas analyte comprises following a series of test and
analysis protocols, testing regimen 501, as illustrated in FIG. 5.
In these embodiments, gas detection and analysis begins by
determining a gas sensor response profile for a control sample 502
comprising one or more known gas species at a known concentration.
Determining a gas sensor response profile comprises steps 503
through 507 in FIG. 5 and begins by applying a bias voltage 503
across all sensors 101 in array 102. The bias voltage is applied
for a sufficient duration to allow the sensors to stabilize to the
environmental conditions (i.e., the sensors exhibit a relatively
unchanging current value). After stabilization, determination of
baseline response data 504 is initiated. After a selected period of
time (typically a few seconds) of determining baseline data, gas
exposure 505 is initiated and continues for a selected period of
time prior to stopping the gas exposure 506. Exposure times may
range from a few milliseconds to hundreds or even thousands of
seconds. It is specifically contemplated that gas sample exposure
times may be for any selected length of time in that range. Sensor
response profile data are determined from the selected baseline
time point, throughout gas exposure, and after the gas exposure
period 507 to the selected end time point. Data determination
periods can range from seconds to hours. In some embodiments of the
invention, it is specifically contemplated that there is no limit
on the time period for data determination. An optional gas sample
purge step 508, such as for example pulsing clean dry air over the
sensors with or without heating, may be employed to clear gas from
the sensors. In some aspects of the invention, determining baseline
and sensor response data (i.e., determining a gas sensor response
profile) comprises recording the data. In other aspects of the
invention, sensor response data are plotted graphically. In still
further aspects of the invention, sensor response data are analyzed
by read out integrated circuits (ROIC) 512 and may be further
analyzed with data analysis algorithms 513.
[0082] In certain embodiments of the invention, one or more
determinations of gas sensor response profiles to the same gas
sample 509 may be employed using testing regimen 501. In additional
embodiments, one or more determinations of gas sensor response
profile to the same gas species at different concentrations in a
gas sample or a portion of a gas sample 510 may be employed.
Similarly, one or more determinations of gas sensor response
profiles to a different control gas sample 511 may be employed
using testing regimen 501. There is no limit on the number of
replicate and/or different gas samples that may be employed in
testing regimen 501.
[0083] Similarly, in various embodiments of the invention, the
duration of gas exposure may be varied. By way of example only, gas
exposure duration times may be about 0.001 sec, 0.01 sec, 0.1 sec,
0.2 sec, 0.5 sec, 1 sec, 2 sec, 5 sec, 10 sec, 20 sec, 30 sec, 60
sec, 120 sec, 300 sec, 400 sec, 500 sec, 1,000 sec or any specific
selected duration between about 0.001 sec and about 1,000 sec or
more inclusive. Any gas exposure duration time may be used in
combination with any number of replicates performed with the same
or different gases.
[0084] In some embodiments of the invention, gas sensor response
profiles can be determined at the same time for all gas sensors 101
that are present in array 102, using ROICs 512, such as for
example, ROICs comprising silicon CMOS logic. Determination of
sensor response profiles provides data for use with analysis
algorithms 513 for comparing test sample sensor response profiles
and control sample sensor response profiles, enabling gas species
identification and quantification.
[0085] After data collection for sensor response profiles
determined with one or more control gas sample 502, response
profiles of sensors 101 on array 102 are determined for a test gas
sample 514, which may include single or multiple unknown gas
species analytes present in a variety of concentrations. Test
sample sensor response profiles are determined as for control gas
sensor response profiles as described above.
[0086] In some aspects of the invention, one or more control gas
sensor response profiles and test sample sensor response profiles
may be stored in a database, and comparing sensor response profiles
comprises comparing one or more test sample response profiles to
one or more control gas response profiles stored in the database.
Comparisons of these stored gas sensor response profiles can be
used to determine the identity and concentration of one or more gas
analyte in a test sample. In additional aspects of the invention,
deconvolution of gas sensor response data enables the
identification and quantification of gases in a test sample.
[0087] Qualitative and quantitative differences and similarities
among control gas sensor response profiles and test sample sensor
response profiles can be determined by using ROIC 512 and data
analysis algorithms 513 and comparing data from control sample 502
sensor response profiles with data from test sample 514 sensor
response profiles. Numerous computational algorithms are available
in the art that are useful for identifying differences in datasets.
Such algorithms are applicable for determination of qualitative and
quantitative differences between sensor response profiles. In
addition, deconvolution algorithms can be used to identify and
quantify individual gases using sensor response profile data
acquired from control gas species and from a test gas sample.
Methods for deconvolution are known in the art. Deconvolution
comprises cross comparison of response profile data from all
sensors 101 in sensor array 102 for each different control gas
sample 502 and test gas sample 514 evaluated using testing regimen
501.
[0088] In embodiments of the invention, comparison of one or more
control gas 502 sensor response profiles with one or more test
sample 514 sensor response profiles is used to detect, identify,
and/or quantify a gas species analyte present in a test sample. In
some aspects of the invention, test sample and control gas sensor
response profiles for comparison are determined under a variety of
different conditions, such as for example, with different types of
diffusion matrices, with different concentrations of gases in a gas
phase or in a vapor phase of a liquid or solid, and/or with
multiple different gas species. Comparisons of control and test
sample gas sensor response profiles determined under a variety of
conditions enable, contribute to, and enhance detection,
identification, and/or quantification of a gas analyte in a test
sample.
[0089] In other embodiments, control gas sensor response profiles
are determined using various types and configurations of diffusion
matrices on gas sensors or are determined using underivatized gas
sensors.
[0090] In further embodiments of the invention, detecting,
identifying, and/or quantifying a gas analyte in a test sample
comprises comparing a test sample sensor response profile,
determined using a specific type of diffusion matrix, with one or
more control gas sensor response profiles determined using the same
type of diffusion matrix. In specific embodiments of the invention,
detection, identification, or quantification of a selected gas
species in a test sample, comprises comparing a test sample sensor
response profile, determined with a specific type of diffusion
matrix, with a control gas sensor response profile determined using
the same type of diffusion matrix with the selected gas species. In
further aspects of the invention, identification of a plurality of
selected gas species in a test sample comprises comparing a test
sample sensor response profile determined with a specific type of
diffusion matrix with a plurality of control gas sensor response
profiles determined with the same type of diffusion matrix and with
the selected gas species of interest.
[0091] In additional embodiments of the invention, a control gas
sensor response profile may be determined using the same sensor as
that used for a test sample sensor response profile, but prior to
determining the sensor response profile with the test gas sample.
In certain embodiments of the invention, multiple sensor response
profiles are determined with a single test sample or a portion of
the same test sample.
[0092] In embodiments of the invention, a gas sample, including a
control sample and a test sample, may comprise a gas, a liquid,
and/or a solid. In some embodiments, a test sample or control
sample comprises individual gases or mixtures of gases. In other
embodiments, a test sample comprises a liquid or solid having a
corresponding vapor component in which a gas analyte is identified
and/or quantified. In some aspects of the invention, a liquid
sample may be a biological sample or an extract of a biological
sample having a corresponding vapor component. In some aspects of
the invention, a biological test sample may be from or may comprise
blood, serum, plasma, tissue, organs, semen, saliva, breath, tears,
sputum, feces, urine, other bodily fluid, hair follicles, skin, or
any sample containing or constituting biological cells.
Alternatively, the sample may not be a biological sample, but may
be a chemical mixture. In other aspects of the invention, the
sample may be an environmental sample, such as an air sample or
other gaseous sample or a liquid sample. A sample may be, or may
comprise, an extract of an environmental sample, such as for
example a soil extract or an extract of a water sample. A water
sample may comprise a gas species analyte that is a biological
toxin or toxicant. In some aspects of the invention, a test sample
comprises a synthetically prepared biological or chemical entity. A
synthetically prepared biological or chemical entity may be a
precursor or product of a biological, chemical or industrial
manufacturing process.
[0093] In some aspects of the invention, a test sample is prepared
using methods that isolate or purify a gas species analyte of
interest, including gas species analytes present in a liquid or
solid sample having a corresponding vapor component, in a form that
will traverse a diffusion matrix on a sensor 101. Methods for
extracting, isolating, or purifying biological molecules and
chemicals from numerous types of samples, including biological,
environmental and industrial or pharmaceutical manufacturing
samples, are available in the art. In other aspects of the
invention, a sample is not purified or extracted prior to
contacting the sample with a gas sensor.
[0094] In some embodiments of the invention a biological test
sample is from an organism. In certain aspects of the invention,
the biological sample is from a patient. In other aspects, the
biological sample is from a medical, pharmaceutical or biological
manufacturing process. In one exemplary aspect of the invention,
the sample can be a biological threat sample collected by military
or first responders. In some embodiments, a sample is from a
patient that has tested positive for a disease, a patient
undergoing treatment, a patient with a tumor or known mutation that
results in the production of a specific disease-associated analyte,
or a patient suspected of having a disease or condition. A
biological sample may also include one or more gas species analytes
indicative of the presence of a pathogen, a virus, a prion, a
fungus, a bacterium, or another organism. In other embodiments of
the invention, a sample may be exhaled breath, such as for example
from an individual (e.g., a patient or a soldier). In further
embodiments, the sample may be collected by sampling ambient air
around an object or a subject in order to determine or identify
recent human activity. Further still, the sample may be collected
by sampling ambient air for the presence of a human or other
organism or for other potential identifying characteristic
associated with a specific biological odor profile.
[0095] The presence of selected gas species analytes in a sample
and/or the amounts of selected analytes in a sample may be
indicative of a disease or condition, may correlate with the
severity of a disease or condition, may be used to evaluate the
response of a patient to a treatment, or may be used to optimize
treatment of a patient. The presence or amount of analyte in a
biological sample may also be examined to evaluate and correlate
the analyte with pharmacokinetics and to adjust the treatment of a
patient such as with a compound or a drug. In some aspects of the
invention, a gas species analyte may be a metabolic by-product or
breakdown product of a treatment compound or a drug.
[0096] Multiple analytes present in a single sample may be queried
using methods of the invention. For example, selected individual
gas nanosensors or groups of gas nanosensors may be derivatized
with selected diffusion matrices through which selected analytes
present in a sample diffuse differently. It is contemplated that
many analytes can be queried at a single time, in a multiplexed
assay format, by using arrays of multiple nanosensors that are
derivatized with selected different types of diffusion
matrices.
[0097] FIG. 6. shows multiple gas sensor response profiles
determined for each of six different volatile organic compound gas
analytes and for water, using a sensor derivatized with linker
structures. For this experiment, the inventors used arrays of
conductometric semiconducting tin oxide (SnO.sub.x) nanotrace
sensors, each nanotrace being 80 nm in width, prepared using
nanoimprint lithography according to methods described in Savoy et
al., U.S. Pat. No. 8,450,131. Gas sensor response profiles were
determined for each of six different volatile organic compound
(VOC) gases and for water 602. The VOC gases included
4-methylbenzaldehyde 601, 4-methyl valeric acid 603, benzaldehyde
604, 2,4-dimethylacetophenone 605, 2-ethyl hexanoic acid 606, and
2-ethylhexanol 607. Sensor response profiles for each gas were
determined using 64 independently addressable nanotrace sensors (56
sample sensors shown in FIGS. 6 and 8 blank control sensors having
no metal oxide that are not shown in the figure).
[0098] Gas sensor response profiles were determined according to
methods described above (FIG. 5), by measuring the electrical
current flowing through the sensor as a function of time, beginning
at a selected time point prior to initiation of gas exposure and
continuing through the length of the gas exposure and for a
selected period of time after exposure of the sensor to the gas was
stopped. Bias voltage was applied and the average value of baseline
current 610 was measured in the 5 sec period prior to initiation of
gas exposure. Linker-derivatized sensors were exposed to a gas
analyte sample at time (t)=0 sec (represented as dashed line 608).
Gas sample exposure was stopped at t=15 sec (represented as dashed
line 609).
[0099] Sensor response data (i.e., current measurements) were
converted to normalized resistance over time using Ohm's law (V=IR)
and the applied bias, typically between 0.01 to 100 V. In this
example, each gas sensor response profile, plotted as normalized
resistance vs. time, was normalized by dividing the value of all
data points on the gas sensor response profile with the average
value of the baseline current 610. For the seven sets of 56 gas
sensor response profiles for each gas in FIG. 6, current
measurements were aligned at t=0 sec.
[0100] The inventors found that multiple determinations of gas
sensor response profiles, using the same gas analyte exhibited the
same pattern (FIG. 6), and gas sensor response profiles differed
among different gas analytes. The sensor response profiles for
water and each gas species 601-607 have characteristics also
exhibited by sensor response profiles determined with sensors
derivatized with other diffusion matrices. The response profiles
display differences among the different gas species 601-607 in the
rate of sensor response rise, the maximum change in sensor
response, and the rate of response fall after stopping gas
exposure. These characteristics of the sensor response profiles
indicate different responses to each gas analyte. The gas sensor
response profile represents a composite effect resulting from the
sample gas diffusion rate, the rate of adsorption of the gas onto
the sensor, the rate of desorption of the gas from the sensor, and
the effect on charge carrier mobility and carrier concentration of
the sensor when gas molecules are adsorbed on the sensor surface.
In some aspects of the invention, exposure of a gas sensor to
certain gas species may result in no sensor response changes, such
as when a gas sensor is exposed to clean dry air. In these aspects,
sensor response is unchanged until the exposure of sensor 101 to a
gas sample is stopped 609.
[0101] Sensor response profiles may also be sensitive to
temperature. In some aspects of the invention, test sample and
control response profiles may be determined at a temperature that
is approximately room temperature (.about.25 C) or at a temperature
that is above or below room temperature.
[0102] FIG. 7A-FIG. 7D. FIG. 7A shows multiple gas sensor response
profiles determined using a sensor derivatized with linker
structures, for each of seven different volatile organic gas
analytes from common industrial solvents. FIG. 7B shows the
averaged gas sensor response profile from all sensors for each gas.
FIG. 7C and FIG. 7D show principal component analysis plots using
data from the gas sensor response profiles. Gas sensor response
profiles with similar characteristics were observed for multiple
replicates of volatile organic compound gas samples such as common
industrial solvents. Fifty-six (56) gas sensor response profiles
determined for each sample gas are shown in FIG. 7A. The gases were
methanol 701, diethylether 702, dichloromethane 703, acetone 704,
hexanes 705, toluene 706, and xylene 707. Similar to the response
profiles shown in FIG. 6, the sensor response profiles for each gas
type 701-707 exhibited differences in the rate of sensor response
rise, the maximum change in sensor response, and the rate of
response fall after stopping gas exposure. These differences are
more clearly apparent when the averaged gas sensor response
profiles from all sensors for each gas are plotted (FIG. 7B).
Switching noise can cause a false response such as that observed
for sensor response profile 708, which was determined for clean dry
air. Switching noise from the gate valve and other electronic
interference artifacts can be removed by shielding. Sensor response
profiles 701-708 were normalized to data collected during the time
interval just before gas analyte exposure (represented by the
shaded area 709 of FIG. 7B).
[0103] In this example, gas sensor response profiles were
determined for all 56 sensors for each gas (FIG. 7A). Sensor
response profiles differed among the gases. However, the response
profile for each sensor in the array was found to be highly
reproducible among the 56 replicate measurements for a single gas
species. Replicate sensors showed high precision (S.D.<2%).
Principal component comparison plots of the falling sum vs. the
rising sum (FIG. 7C) and the rising sum vs. the normalized peak
height (FIG. 7D) show the differences in the response profiles
among the individual sensor responses. In addition, the plots
illustrate that sample gases with similar chemical functional
groups such as nonpolar aliphatic and aromatic hydrocarbons 705,
706, 707 behave similarly and display the least differences in the
respective response profiles. In embodiments of the invention,
improved separation of these gas species can be achieved by
analyzing sensor response profiles determined with sensors that are
derivatized with different types of diffusion matrices, resulting
in enhanced detection and quantification of specific gas analyte
species.
[0104] To determine the gas sensor response profiles illustrated in
FIG. 6 and FIG. 7, a sample of solvent vapor at the full vapor
pressure at room temperature was isolated in an impinger vessel or
directly connected from a lecture bottle of the gas. Gas exposure
was accomplished by directing a fixed flow rate of a carrier gas
(measured as sccm) into the liquid impinger using a mass flow
controller. Examples of carrier gases include clean dry air, argon,
or other inert gases. This sample stream was recombined with the
stream of carrier gas then directed over the gas sensor for fixed
time intervals ranging from 1 sec-300 sec. The concentration of the
gas analyte sample in the carrier gas is controlled using mass flow
controllers. In some embodiments of the invention, test samples may
be diluted with or directly combined with a background carrier gas
that is directed over the surface of gas sensor 101.
[0105] In some experiments, responses were determined for each
sensor as the ratio R.sub.s/R.sub.0 converted from the measured
sample current (I.sub.s) using the applied bias voltage V and Ohm's
law and a normalization procedure. Current measurements were first
determined at a fixed voltage, typically in the range of 0.1V-1V.
Useful fixed voltages may be as low as 1 microVolt to as high as
hundreds of Volts. In additional embodiments of the invention,
normalization involves dividing each calculated resistance value of
the response (R.sub.s) with the measured resistance just prior to
sample exposure (R.sub.0).
[0106] FIG. 8A-FIG. 8C. FIG. 8A and FIG. 8B illustrate
concentration profile curves representing the concentration
distribution of a gas analyte between a gas source and a sensor
derivatized with an exemplary biomolecular diffusion matrix, during
gas exposure and diffusion of gas analytes to the sensor (FIG. 8A)
and after stopping gas exposure and during diffusion of gas
analytes from the sensor (FIG. 8B). FIG. 8C shows the corresponding
gas sensor response profile and the position in time corresponding
to each concentration profile curve. In FIG. 8A and FIG. 8B, for
ease of viewing, biomolecular diffusion matrix is represented with
shaded rectangles, and the gas analyte is represented as horizontal
arrows. Concentration (arb.) (y-axis) refers to concentration
represented as arbitrary units. Distances from gas source (x-axis)
are in arbitrary units.
[0107] FIG. 8A is a one-dimensional concentration profile
simulation 801 for gas analyte 106, at different times during gas
exposure and diffusion of gas analytes to the sensor.
Concentrations are expressed as the log concentration of the gas
analyte in arbitrary units (arb.) (y-axis) as a function of the
distance from gas source in arbitrary units (x-axis). In FIG. 8A,
each concentration profile curve (802, 803, 804, 805, 806)
represents the concentration of the gas analyte at positions
between the gas source and sensor 101B at a specific time during
gas sample exposure when gas analyte molecules are diffusing
through diffusion matrix 104B and becoming adsorbed to the surface
of gas sensor 101B. An exemplary, sample exposure duration may be
30 sec, and the concentration profile curves 802-806 may represent
gas concentration profiles determined at, for example, 1 sec, 5
sec, 10 sec, 15 sec, and 30 sec, respectively, following initiation
of gas sample exposure. Gas analyte concentration at the sensor
surface increases with increasing time of gas exposure, e.g., curve
806 representing the longest time after initiation of gas sample
exposure. The simulation is primarily based on Fick's diffusion
law, treating the biomolecular diffusion matrix with a diffusion
coefficient distinct from the region between the outermost surface
of biomolecular diffusion matrix 104B on sensor 101B and the gas
analyte source (at position 0 on the x-axis). The simulation takes
into account diffusion parameters and the adsorption and desorption
rates of the gas analyte on the surface of the sensor.
[0108] FIG. 8B shows a one-dimensional concentration profile
simulation 810 for gas analyte 106, at specific times after
exposure to the gas sample is stopped, expressed as the log
concentration of the gas analyte in arbitrary units (arb.) (y-axis)
as a function of the distance from gas source (x-axis). In this
period, the carrier gas flowing over sensor 101B does not contain
gas analyte. As such, some gas analytes desorb from the surface of
gas sensor 101B and diffuse away, with carrier gas, from the sensor
through diffusion matrix 104B. The concentration profile curves
(811, 812, 813, 814, 815) represent gas concentrations at
progressively longer times, at positions between the gas source and
the outermost surface of biomolecular diffusion matrix 104B, after
gas analyte exposure is stopped. Gas concentration at sensor 101B
is highest at the shortest time point after gas exposure has
stopped, represented by curve 811, and is lowest at the longest
time point after gas exposure has stopped, represented by curve
815.
[0109] FIG. 8C shows the corresponding gas sensor response profile
and the position in time corresponding to each concentration
profile curve 802, 803, 804, 805, 806, 811, 812, 813, 814, and 815
shown in FIGS. 8A and 8B. The intersection of a concentration
profile curve with the gas sensor response profile curve correlates
the amount of gas adsorbed on sensor 101B with the sensor response
at that time, during diffusion of gas analyte to the sensor (FIG.
8A, 802-806) and during diffusion of gas analyte away from the
sensor (FIG. 8B, 811-815). Sensor response is greatest at the time
interval between 806 and 811 when the concentration of gas analyte
at the sensor surface is near saturation (i.e., the maximum number
of sensor surface sites are occupied by gas analyte 106.
[0110] FIG. 9. shows a one-dimensional simulation of
sensor-adsorbed concentration of a single gas analyte species with
gas sensors having different exemplary biomolecular diffusion
matrices, after a square pulse injection of a gas sample.
Concentration (arb.) refers to concentration represented as
arbitrary units. Time (x-axis) is in arbitrary units. FIG. 9
depicts the effect of different diffusion matrices on the
concentration profile of a monotonic gas analyte adsorbed on the
surface of a gas sensor. Simulated gas sensor concentration
profiles 901 show gas concentration generated for five replicate
exposures (square pulse) 902 of the same gas analyte. The
simulations include concentration profiles representing three
different biomolecular diffusion matrices that result in gas
analyte diffusion coefficients of 200 cm.sup.2/s 903; 20 cm.sup.2/s
904; and 2 cm.sup.2/s 905. Differences in the time for the gas
analyte concentration at the sensor to increase and reach a
maximum, in the peak magnitude of gas analyte concentration at the
sensor surface, and in the time required for the gas analyte
concentration at the sensor to fall after termination of gas sample
exposure are observed for the different biomolecular diffusion
matrices. For sensor concentration profiles determined when the gas
analyte diffusion coefficient is relatively larger (e.g., 903) the
change in the gas analyte concentration at the sensor is marked by
a rapid rise, followed by a rapid decrease of gas analyte
concentration at the sensor after stopping gas exposure. In
contrast, a sensor having a biomolecular diffusion matrix that
results in a relatively smaller gas analyte diffusion coefficient,
displays a concentration profile as depicted by 905 with relatively
slower increases in the gas analyte concentration at the sensor
followed by a corollary slower return back to the pre-exposure
concentration prior to gas exposure. Profiles 903 and 904 are
labeled at two locations on each curve.
[0111] FIG. 10A-FIG. 10D. FIG. 10A and FIG. 10B illustrate
concentration profile curve simulations representing the
concentration distribution of a gas analyte between a gas source
and a sensor derivatized with one type of exemplary biomolecular
diffusion matrix. FIG. 10A shows concentration profile curves
during gas exposure and diffusion of gas analytes to the sensor.
FIG. 10B shows concentration profiles curves after stopping gas
exposure and during diffusion of gas analytes from the sensor. FIG.
100 and FIG. 10D illustrate concentration profile curves
representing the concentration distribution of the same gas analyte
between a gas source and a sensor derivatized with a different type
of exemplary biomolecular diffusion matrix. FIG. 100 shows
concentration profile curves during gas exposure and diffusion of
gas analytes to the sensor, and FIG. 10D shows concentration
profiles curves after stopping gas exposure and during diffusion of
gas analytes from the sensor. For ease of viewing, the biomolecular
diffusion matrices are shown as shaded rectangles and the gas
analyte is represented as horizontal arrows. Concentration (arb.)
refers to concentration represented as arbitrary units. Distances
from gas source (x-axis) are in arbitrary units.
[0112] The simulations illustrate that different types of diffusion
matrices present on a gas sensor affect the diffusion of a gas
analyte to and from the sensor, thereby affecting the gas sensor
response profile. FIG. 10A shows concentration profile simulation
1001 during gas exposure and during diffusion of gas analyte 106
through biomolecular diffusion matrix 104B to sensor 101B.
Concentration profile curves 1002, 1003, 1004, 1005, and 1006
represent gas concentration profiles determined at, for example, 1
sec, 5 sec, 10 sec, 15 sec, and 30 sec, respectively, after
initiating gas exposure. FIG. 10B shows the concentration profile
simulation 1010 after stopping gas exposure and during diffusion of
gas analyte 106 away from sensor 101B and through biomolecular
diffusion matrix 104B. Concentration profile curves 1011, 1012,
1013, 1014, and 1015 represent gas concentration profiles
determined at, for example, 1 sec, 5 sec, 10 sec, 15 sec, and 30
sec, respectively, after stopping gas exposure.
[0113] FIG. 10C and FIG. 10D illustrate concentration profile
simulations 1016 and 1022 for sensor 101C having a different
biomolecular diffusion matrix 104C. Here, biomolecular diffusion
matrix 104C has a different diffusion coefficient than biomolecular
diffusion matrix 104B of FIG. 10A and FIG. 10B. FIG. 10C shows
concentration profile simulation 1016 during gas exposure and
during diffusion of gas analyte 106 through biomolecular diffusion
matrix 104C to sensor 101C. Concentration profile curves 1017,
1018, 1019, 1020, and 1021 represent gas concentration profiles
determined at, for example, 1 sec, 5 sec, 10 sec, 15 sec, and 30
sec, respectively, after initiating gas exposure. FIG. 10D
illustrates the concentration profile simulation 1022 after
stopping gas exposure and during diffusion of gas analyte 106 away
from sensor 101C and through biomolecular diffusion matrix 104C.
Concentration profile curves 1023, 1024, 1025, 1026, and 1027
represent gas concentration profiles determined at, for example, 1
sec, 5 sec, 10 sec, 15 sec, and 30 sec, respectively, after
stopping gas exposure.
[0114] FIG. 11 shows gas sensor response profiles determined for
exposure of gas sensors to the gas analyte N-methyl-2-pyrrolidone,
at three different relative gas concentrations. The sensors were
linker-derivatized or derivatized with one of four different
biomolecular diffusion matrices covalently coupled to their
surfaces. Raw current (.mu.A) was measured for determining
individual sensor responses, and data were plotted as a function of
time (seconds). Time on the x-axis represents the total elapsed
time during the recording of all sensor responses.
[0115] In this example, sensors were derivatized with linker
structures or one of two different proteins or two different
oligopeptides. Proteins and oligopeptides were spotted over
selected regions that included multiple sensors on an array of
semiconducting tin oxide nanotrace sensors. In this example, all
sensors 101 were present on the same monolithic array 102. For
derivatizing sensors, spotting was carried out by first silanizing
the entire sensor array using dimethylepoxyoctylsilane (Gelest
Inc.; Morrisville, Pa., USA). The silane couples to free --OH
groups on the surface of the tin oxide. After .about.1 min of
exposure to silane, oligopeptide and protein molecules were spotted
on the sensor array surface and left on the sensor surface for
.about.1 hr, after which excess fluid was rinsed from the surface.
Each oligopeptide or protein spot covered between 7 and 10 sensors.
Linker-derivatized nanotraces were modified only with silane were
present between regions of the sensor array that comprised sensors
having a biomolecular diffusion matrix. In addition, one row of
sensors having no tin oxide was used as a control or open circuit
sensor in which no current passes during determination of a
response.
[0116] As described for some embodiments of testing regimen 501,
ten consecutive gas sensor response profiles to the same gas
species sample 509 were determined, and ten sensor response
profiles were determined for the same gas species at different
concentrations 510. Response profiles 1101A, 1102A, 1103A, and
1104A were determined using four different sensors 101A derivatized
by linker silane treatment 103; sensor response profiles 1101B,
1102B, and 1103B were determined using three different sensors
derivatized with a biomolecular diffusion matrix comprising a 7-mer
peptide sequence, Fmoc-DLESFLD (MW.about.946 Da); sensor response
profiles 1101C, 1102C, and 1103C were determined using three
different sensors derivatized with a biomolecular diffusion matrix
comprising an 8-mer peptide, Fmoc-RVNEWVID (MW.about.1,156 Da);
sensor response profiles 1101D, 1102D, and 1103D were determined
using three different sensors derivatized with a biomolecular
diffusion matrix comprising BSA (MW.about.66,500 Da); and sensor
response profiles 1101E, 1102E, and 1103E were determined using
three different sensors derivatized with a biomolecular diffusion
matrix comprising an anti-biotin antibody (MW.about.160,000 Da).
For each set of sensor types, response profiles were determined
using three different relative concentrations of
N-methyl-2-pyrrolidone that were diluted from concentrations of 2%,
5%, and 7% of saturated vapor.
[0117] In this example, the sensor raw current (.mu.A) was measured
for determining individual sensor responses, and data were plotted
as a function of time (seconds). Sensor responses were recorded
throughout the entire time during the multiple exposures at the
three different gas analyte concentrations 510 using testing
regimen 501. Ten replicate gas exposures of the same gas 509 at the
same concentration for all sensors and all derivatizations were
performed using testing regimen 501. After gas exposure initiation
505, gas exposure lasted 30 sec and was followed by a gas exposure
stop 506. The final (tenth) exposure of the same gas 509 was
delayed for a longer period of time before data determination of
the next set of ten replicate exposures 509 at the higher gas
concentration 510 using testing regimen 501. The first ten gas
exposures, at the lowest relative concentration, are represented by
the ten sensor response profiles in regions 1151 followed by a
delay period 1152 indicated on the horizontal line above the
x-axis. The second ten gas exposures, at the middle relative
concentration, are represented by the ten sensor response profiles
in regions 1153 followed by delay period 1154, which preceded the
third set of ten gas exposures, at the highest relative
concentration, which is represented by the ten sensor response
profiles in region 1155. The response profile 1156 for nanotraces
without semiconducting metal oxide between electrodes shows a
minimal response with much lower current magnitude at the same bias
voltage relative to sensors with metal oxide semiconductor.
[0118] As was observed in other experimental examples, the data
shown in FIG. 11 demonstrate that replicate sensors that are
derivatized with the same type of diffusion matrix had similar
response profile features in response to the same gas analyte,
including sensor response rise time, peak magnitude of sensor
response, and sensor response fall time. This was observed for all
replicate gas exposures to sensors with the same type of
biomolecular diffusion matrix, regardless of the gas analyte
concentration. In FIG. 11, the five different types of sensors
(four derivatized with different biomolecular diffusion matrices
and one underivatized) each had different initial current values,
which was a result of a change in the current carrying capacity of
the nanotrace sensor in response to the different amount of
diffusion matrix biomolecules being present on the sensor surface
relative to the underivatized sensors and other factors such as the
physical dimensions and intrinsic conductivity of the metal oxide
sensing material.
[0119] Molecular diffusion matrix properties, e.g., molecular
weight, chemical composition, and thickness, influence the rate of
diffusion of a gas analyte to the sensor. Consequently, sensor
response profiles for a given gas analyte are different depending
on the type of diffusion matrix present on the sensor. The data for
these different sensor response profiles, determined with one or
more control and test gas samples are used with analysis algorithms
513 to enable the identification and quantification of each gas
analyte in a test sample. Qualitative and quantitative differences
between control and test sample gases can be determined by using
ROIC 512 and data analysis algorithms 513 and comparing data
recorded for control sample 502 sensor response profiles and test
sample 514 sensor response profiles. Numerous computational
algorithms are available in the art that are useful for identifying
differences in datasets. By way of example, the replicate control
gas sensor profiles and the associated data sets shown in FIG. 11,
determined for the control gas N-methyl-2-pyrrolidone at different
concentrations and with sensors having different types of
biomolecular diffusion matrices, may be compared with sensor
response profiles similarly determined for a test gas sample as
part of an analysis to identify and quantify gas analytes in a test
gas sample.
[0120] FIG. 12A-FIG. 12E show five series of gas sensor response
profiles in response to the gas analyte acetone, each series
determined with a gas sensor derivatized with a different type of
diffusion matrix. All y-axes represent normalized response
(R/R.sub.0), and all x-axes represent time (seconds). The sensor
response profiles were determined using four different
concentrations of saturated acetone vapor pressure (0.07%, 1201;
0.05%, 1202; 0.02%, 1203; and 0.01%, 1204) with five different
types of gas sensors. The concentration of acetone was controlled
by the ratiometric mixing of a clean, dry air stream with a
fraction of a stream that is redirected through an impinger vessel
containing liquid acetone. The vapor that is carried from this
stream is mixed with the clean, dry air using a mass flow
controller to yield the desired concentration ratios. The
concentrations were approximately a percentage of acetone-saturated
vapor pressure at the temperature and pressure of the measurement
system. For this experiment, sensor current was converted to
resistance, which is calculated from Ohm's law (V=IR) where the
applied bias voltage V is divided by the raw sample current I.sub.s
for each point (R=V/I.sub.s). After calculating the sample
resistance, the normalized resistance ratio R/R.sub.0 was
determined by dividing each resistance value by the average of the
first few resistance measurements (R.sub.0) just prior to gas
exposure.
[0121] Sensor response profiles plotted in each of FIG. 12A-FIG.
12E represent the averaged, normalized response profile of multiple
sensors having the same type of derivatization, to each
concentration of gas analyte. FIG. 12A shows the averaged,
normalized sensor responses, for four gas concentrations (1201A,
1202A, 1203A, 1204A), for all sensors derivatized with silane
treatment. FIG. 12B shows the averaged, normalized sensor responses
for four gas concentrations (1201B, 1202B, 1203B, 1204B), for all
sensors derivatized with the 7-mer peptide, Fmoc-DLESFLD
(MW.about.946 Da). FIG. 12C shows the averaged, normalized sensor
response for four gas concentrations (1201C, 1202C, 1203C, 1204C),
for all sensors derivatized with the 8-mer peptide, Fmoc-RVNEWVID
(MW.about.1,156 Da). FIG. 12D shows the averaged, normalized sensor
responses for four gas concentrations (1201D, 1202D, 1203D, 1204D),
for all sensors derivatized with BSA (MW.about.66,500 Da). FIG. 12E
shows the averaged, normalized sensor responses for four gas
concentrations (1201E, 1202E, 1203E, 1204E), for all sensors
derivatized with anti-biotin antibody (MW.about.160,000 Da).
[0122] Like the raw current determinations illustrated in FIG. 11
for the gas analyte N-methyl-2-pyrrolidone, differences in the
sensor response profile features were observed among the sensors
with different types of derivatization (FIG. 12A-FIG. 12E) shown
here for the gas analyte acetone. Differences were observed in the
sensor response rise time, the peak magnitude of the sensor
response, and the sensor response fall time. The differences in the
response profiles are attributed to differences in the diffusion
rate for acetone gas through the different types of diffusion
matrices. The peak magnitude of the response is influenced by the
molecular weight of the diffusion matrix molecules, and hence the
thickness and density of the diffusion layer. For example, the high
molecular weight anti-biotin antibody (FIG. 12E) shows the lowest
peak magnitude of sensor response among the different biomolecular
diffusion matrices examined. In the normalized, averaged response
profiles of FIG. 12, the pressure required for check valve opening,
caused a delay in the sensor response, which became more pronounced
for lower gas analyte concentrations. In other invention
embodiments, removing the requirement for pressure thresholds in
the measurement system eliminated the pressure dependence of the
diffusion rate from the gas source to the gas sensor.
[0123] FIG. 13A-FIG. 13E show five series of gas sensor response
profiles in response to the gas analyte xylene, each series
determined with a gas sensor derivatized with a different type of
diffusion matrix. All y-axes represent normalized response
(R/R.sub.0), and all x-axes represent time (seconds).
[0124] The sensor response profiles were determined using four
different dilution percentages of a vessel containing saturated
xylene vapor (0.07%, 1301A, 1301B, 1301C, 1301D, 1301E; 0.05%,
1302A, 1302B, 1302C, 1302D, 1302E; 0.02%, 1303A, 1303B, 1303C,
1303D, 1303E; and 0.01%, 1304A, 1304B, 1304C, 1304D, 1304E) with
gas sensors derivatized with five different types of diffusion
matrices. The sensor derivatizations for the series in FIG.
13A-FIG. 13E were the same as those described for FIG. 12A-FIG.
12E. Experimental methods and data analyses were the same as
described for FIG. 12. Sensor response profiles plotted in each of
FIG. 13A-FIG. 13E represent the averaged, normalized response
profile, to each concentration of gas analyte, for multiple sensors
derivatized with the same type of diffusion matrix. As was observed
for acetone in FIG. 12, differences among the gas sensor response
profiles to xylene were also observed. The differences in the
response profile for gas analyte acetone (FIG. 12A-FIG. 12E) and
chemically-distinct gas analyte xylene (FIG. 13A-FIG. 13E) provide
exemplary data that can be used with deconvolution algorithms 513
to numerically separate the sensor response of one gas species
analyte from a different gas species analyte, resulting in a gas
analyte signature for identifying and quantifying one or more gas
analyte in a test sample.
[0125] FIG. 14A-FIG. 14F show six series of gas sensor response
profiles, determined using silane-derivatized sensors in response
to six different gas analytes, each analyte at three different
concentrations of saturated gas vapor pressure (0.07%, 0.05%, and
0.02%). FIG. 14A, acetone; FIG. 14B, ammonia; FIG. 14C,
dimethylacetophenone; FIG. 14D, N-methyl-2-pyrrolidone; FIG. 14E,
toluene; FIG. 14F, xylene. Sensor response profiles plotted in each
of FIG. 14A-FIG. 14F represent the average of multiple sensor
response profiles for sensors with the same derivatization, and
were determined for each gas concentration. All y-axes represent
normalized response (R/R.sub.0), and all x-axes represent time
(seconds).
[0126] The sensor response profiles for 0.07%, 0.05%, and 0.02%
concentrations of saturated gas vapor pressure are respectively;
acetone--1401, 1402, 1403; ammonia--1404, 1405, 1406;
dimethylacetophenone--1407, 1408, 1409;
N-methyl-2-pyrrolidone--1410, 1411, 1412; toluene--1413, 1414,
1415; xylene--1416, 1417, 1418). Here, differences among response
profiles of silane-derivatized sensors to six different gas
analytes were observed.
[0127] FIG. 15A-FIG. 15F show six series of gas sensor response
profiles, determined using sensors derivatized with the 8-mer
peptide RVNEWVID as a diffusion matrix, in response to six
different gas analytes, each analyte at three different
concentrations of saturated gas vapor pressure (0.07%, 0.05%, and
0.02%). FIG. 15A, acetone; FIG. 15B, ammonia; FIG. 15C,
dimethylacetophenone; FIG. 15D, N-methyl-2-pyrrolidone; FIG. 15E,
toluene; FIG. 15F, xylene. Sensor response profiles plotted in each
of FIG. 15A-FIG. 15F represent the average of multiple sensor
response profiles determined for each gas concentration. All y-axes
represent normalized response (R/R.sub.0), and all x-axes represent
time (seconds).
[0128] The sensor response profiles for 0.07%, 0.05%, and 0.02%
concentrations of saturated gas vapor pressure are respectively;
acetone--1501, 1502, 1503; ammonia--1504, 1505, 1506;
dimethylacetophenone--1507, 1508, 1509; N-methyl-2-pyrrolidone
--1510, 1511, 1512; toluene--1513, 1514, 1515; xylene--1516, 1517,
1518. In this example, sensor response profiles determined with
this diffusion matrix were found to be different depending on the
gas analyte being detected.
[0129] FIG. 16A-FIG. 16F show six series of gas sensor response
profiles, determined using sensors derivatized with the relatively
large protein anti-biotin antibody as a diffusion matrix, in
response to six different gas analytes, each analyte at three
different concentrations of saturated gas vapor pressure (0.07%,
0.05%, and 0.02%). FIG. 16A, acetone; FIG. 16B, ammonia; FIG. 16C,
dimethylacetophenone; FIG. 16D, N-methyl-2-pyrrolidone; FIG. 16E,
toluene; FIG. 16F, xylene. Sensor response profiles plotted in each
of FIG. 16A-FIG. 16F represent the average of multiple sensor
response profiles determined for each gas concentration. All y-axes
represent normalized response (R/R.sub.0), and all x-axes represent
time (seconds).
[0130] The sensor response profiles for 0.07%, 0.05%, and 0.02%
concentrations of saturated gas vapor pressure are respectively;
acetone--1601, 1602, 1603; ammonia--1604, 1605, 1606;
dimethylacetophenone--1607, 1608, 1609;
N-methyl-2-pyrrolidone--1610, 1611, 1612; toluene--1613, 1614,
1615; xylene--1616, 1617, 1618). As observed in the experiment
described in FIG. 15, for all sensors derivatized here with the
biomolecular diffusion matrix, anti-biotin, gas sensor response
profiles differed depending on the gas analyte being detected.
[0131] For the sensor response profiles shown in FIGS. 14-16, the
y-axes for corresponding plots with the same gas (e.g., ammonia in
FIG. 14B, FIG. 15B, and FIG. 16B) have the same scale, so as to
effectively demonstrate the differences in sensor response profiles
determined with the same gas but with different sensor
derivatization (FIG. 14, silane-derivatized; FIG. 15, 8-mer
peptide; FIG. 16, anti-biotin antibody). The sensor response
profiles for the six gas analytes shown in (FIG. 14-FIG. 16) and
the differences among them provide exemplary data that can be used
to identify and quantify one or more gas analyte after implementing
data analysis deconvolution algorithms 513 that can numerically
separate the sensor response of one gas analyte from another.
[0132] In some embodiments of the invention, a ratiometric
comparison of gas sensor response profiles (FIG. 17-FIG. 20) is
another exemplary means for determining and illustrating
differences among gas sensor response profiles and for identifying
and quantifying different gas analytes in a gas mixture. A
ratiometric comparison plot represents the ratio of a sensor
response profile determined with a sensor derivatized with one type
of diffusion matrix to a sensor response profile determined with a
sensor derivatized with a different type of diffusion matrix. In
some embodiments of the invention in which multiple sensors are
used, the averaged, normalized sensor response profiles are used
for ratiometric comparisons. Gas sensor response profile data
obtained using various, selected control gas species, various gas
concentrations, and various diffusion matrices on sensors provide
data for deconvolution algorithms 513 that are used to identify and
quantify gas analytes in a test gas sample. In some aspects of the
invention, higher numbers of sensor response profiles with
different types of diffusion matrices may be used to increase the
accuracy of identifying and quantifying gas analytes in a test gas
sample.
[0133] FIG. 17A-FIG. 17J show ten ratiometric comparison plots of
sensor response profiles determined after exposure of sensors to
the gas analyte xylene. Sensor response profile ratios include FIG.
17A, linker-derivatized sensor to sensor derivatized with peptide
DLESFLD (1701); FIG. 17B, linker-derivatized sensor to sensor
derivatized with peptide RVNEWVID (1702); FIG. 17C,
linker-derivatized sensor to sensor derivatized with BSA (1703);
FIG. 17D, linker-derivatized sensor to sensor derivatized with
anti-biotin (1704); FIG. 17E, sensor derivatized with peptide
DLESFLD to sensor derivatized with peptide RVNEWVID (1705); FIG.
17F, sensor derivatized with peptide DLESFLD to sensor derivatized
with BSA (1706); FIG. 17G, sensor derivatized with peptide DLESFLD
to sensor derivatized with anti-biotin (1707); FIG. 17H, sensor
derivatized with peptide RVNEWVID to sensor derivatized with BSA
(1708); FIG. 17I, sensor derivatized with peptide RVNEWVID to
sensor derivatized with anti-biotin (1709); FIG. 17J, sensor
derivatized with BSA to sensor derivatized with anti-biotin (1710).
All y-axes represent normalized response (R/R.sub.0), and all
x-axes represent time (seconds).
[0134] The ratiometric comparison plots shown in FIG. 17A-FIG. 17J
were used for comparing sensor response profiles determined with
the gas analyte xylene, at 0.07% of saturated vapor near standard
temperature and pressure, each response profile determined with a
linker-derivatized sensor or with a sensor having a different
biomolecular diffusion matrix. Ratiometric comparison illustrated
that differences were present between all sensor response profiles
compared (FIG. 17 A-FIG. 17J).
[0135] FIG. 18A-FIG. 18J show ten ratiometric comparison plots of
sensor response profiles determined after exposure of sensors to
the gas analyte xylene. All sensors used in these examples were
underivatized bare sensors (i.e., no silane linker treatment). The
same sensors as were described in the description for FIG. 17A-FIG.
17J were exposed to xylene, but here, gas exposure to the
underivatized bare sensors occurred prior to the coupling of the
biomolecular diffusion matrices to the sensors or prior to any
silane treatment of sensors. All y-axes represent normalized
response (R/R.sub.0), and all x-axes represent time (seconds).
[0136] Sensor response profiles for the underivatized bare sensors
were determined with the gas analyte xylene, at 0.07% of saturated
vapor. Ratiometric comparison plots of these profiles are shown in
FIG. 18A-FIG. 18J. The plots in FIG. 18 (1801, 1802, 1803, 1804,
1805, 1806, 1807, 1808, 1809, 1810) represent comparisons of sensor
profiles determined with the exact same sensors described in the
corresponding plots for FIG. 17A-FIG. 17J, but using response
profiles determined before any derivatization or silane treatment
of sensors. As expected, the ratiometric comparisons indicated
extremely small differences among response profiles of
underivatized/untreated sensors.
[0137] FIG. 19A-FIG. 19J show ten ratiometric comparison plots of
sensor response profiles determined after exposure of sensors to
the gas analyte toluene. Sensor response profile ratios include
FIG. 19A, linker-derivatized sensor to sensor derivatized with
peptide DLESFLD (1901); FIG. 19B, linker-derivatized sensor to
sensor derivatized with peptide RVNEWVID (1902); FIG. 19C,
linker-derivatized sensor to sensor derivatized with BSA (1903);
FIG. 19D, linker-derivatized sensor to sensor derivatized with
anti-biotin (1904); FIG. 19E, sensor derivatized with peptide
DLESFLD to sensor derivatized with peptide RVNEWVID (1905); FIG.
19F, sensor derivatized with peptide DLESFLD to sensor derivatized
with BSA (1906); FIG. 19G, sensor derivatized with peptide DLESFLD
to sensor derivatized with anti-biotin (1907); FIG. 19H, sensor
derivatized with peptide RVNEWVID to sensor derivatized with BSA
(1908); FIG. 19I, sensor derivatized with peptide RVNEWVID to
sensor derivatized with anti-biotin (1909); FIG. 19J, sensor
derivatized with BSA to sensor derivatized with anti-biotin (1910).
All y-axes represent normalized response (R/R.sub.0), and all
x-axes represent time (seconds).
[0138] The ratiometric comparison plots shown in FIG. 19A-FIG. 19J
were used for comparing sensor response profiles determined with
the gas analyte toluene, at 0.07% of saturated vapor near standard
temperature and pressure, each response profile determined with a
linker-derivatized sensor or with a sensor having a different
biomolecular diffusion matrix. Ratiometric comparison illustrated
that differences were present between all sensor response profiles
compared (FIG. 19A-FIG. 19J).
[0139] FIG. 20A-FIG. 20J show ten ratiometric comparison plots of
sensor response profiles determined after exposure of sensors to
the gas analyte toluene. All sensors used in these examples were
underivatized bare sensors (i.e., no silane linker treatment). The
same sensors as were described in the description for FIG. 19A-FIG.
19J were exposed to toluene, but here, gas exposure to the
underivatized bare sensors occurred prior to the coupling of the
biomolecular diffusion matrices to the sensors or prior to any
silane treatment of sensors. All y-axes represent normalized
response (R/R.sub.0), and all x-axes represent time (seconds).
[0140] Sensor response profiles for the underivatized and untreated
bare sensors were determined with the same gas analyte toluene, at
0.07% of saturated vapor. Ratiometric comparison plots of these
profiles are shown in FIG. 20A-FIG. 20J. The plots in FIG. 20
(2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010)
represent comparisons of sensor profiles determined with the exact
same sensors described in the corresponding plots for FIG. 19A-FIG.
19J, but using profiles determined before any derivatization or
silane treatment of sensors. As expected, the ratiometric
comparisons indicated small differences among response profiles of
underivatized/untreated sensors.
[0141] The corresponding ratiometric comparison plots in FIG. 17
and FIG. 19 (e.g., 1701 vs. 1901, 1702 vs. 1902, 1703 vs. 1903,
1704 vs. 1904, 1705 vs. 1905, 1706 vs. 1906, 1707 vs. 1907, 1708
vs. 1908, 1709 vs. 1909, 1710 vs. 1910) show differences that are
attributable to the differential interaction of the two different
gases with the specific type of diffusion matrix present on a
sensor. These differences represent exemplary data that enable
identification and quantification of gas analytes in test
samples.
[0142] FIG. 21 shows five series of gas sensor response profiles
determined with the gas analyte N-methyl-2-pyrrolidone using two
different sensors derivatized with linkers 2101A, 2102A and three
different sensors derivatized with an anti-biotin biomolecular
diffusion matrix 2101D, 2102D, 2103D. Sensor response was plotted
as raw current vs. time. Time on the x-axis represents the total
elapsed time during the recording of all sensor responses using
testing regimen 501.
[0143] Each series of sensor response profiles includes six
replicate response profiles 509 recorded for each of eleven
different gas exposure time defined as the time interval between
initiating gas sensor exposure 505 to stopping gas sensor exposure
506. Sensor responses were recorded throughout the entire time
during the multiple gas exposures. Regions of the graph with
different gas exposure times are indicated on the horizontal line
above the x-axis and included 2 sec 2120, 3 sec 2121, 4 sec 2122, 5
sec 2123, 8 sec 2124, 10 sec 2125, 20 sec 2126, 30 sec 2127, 60 sec
2128, 120 sec 2129, and 300 sec 2130 exposure times. The data show
that response profiles determined with multiple sensors having the
same diffusion matrix are very similar and are highly reproducible.
Additionally, sensor response data demonstrated that the gas
exposure time required to reach sensor saturation was affected by
the presence of the diffusion matrix. Sensor saturation occurs when
all available reactive sites on the sensor surface are occupied and
the rates of adsorption and desorption have reached equilibrium.
The amplitude of the response displacement stops increasing or
decreasing and levels off even though gas exposure continues,
(i.e., after initiating gas exposure 505, but before stopping gas
exposure 506). Linker-derivatized sensors 2101A, 2102A saturate
between 60 sec and 120 sec (2131). In contrast, sensors derivatized
with anti-biotin saturated between 120 sec and 300 sec as
illustrated by the replicate responses 2132. The time required to
reach saturation can be defined as an exemplary representation of
the response profile data and may be used for identifying a
specific gas analyte in a test sample using data analysis
algorithms 513.
[0144] FIG. 22 shows a concentration dilution series of sensor
response profiles determined for exposure of linker-derivatized
sensors to methanol. The plot illustrates quantitative dependence
of sensor response profiles on gas analyte concentration. The left
side y-axis is normalized peak area, the right side y-axis is log
normalized peak area, and x-axis is vapor concentration estimated
by 1/2-fold dilutions (ppm).
[0145] In embodiments of the invention, the sensitivity and
detection limit of the gas nanosensor is dependent on the
differences in the diffusion rate of the control gas species and
the diffusion rate of the test sample gas analyte for a given
diffusion matrix. FIG. 22 illustrates a concentration profile trend
for one sensor 2201 in which profiles were determined for multiple
exposures of methanol at different saturated vapor pressures of
methanol (in parts per million, ppm). In this experiment the first
concentration profile was determined using a low flow rate carrier
gas stream at 50 sccm through an impinger that was initially filled
with 100% saturated vapor of methanol .about.167,000 ppm at
standard temperature and pressure. The flow rate of 50 sccm and
vessel volume of 60 ml were chosen such that the concentration of
methanol gas would be diluted approximately in half during each gas
exposure interval. Subsequent concentration profiles were also
determined after a dilution of the gas concentration.
[0146] For sensor responses determined with high concentrations of
gas, the length of the time interval between initiating gas
exposure 505 and stopping gas exposure 506 was insufficient to
reach sensor saturation with adsorbed gas analyte molecules. The
increase in magnitude of the response in subsequent exposures
illustrates that saturation was not been reached at the highest gas
concentrations examined. Furthermore, the length of time between
subsequent exposures was insufficient to permit gas molecules to
diffuse from the sensor back through the diffusion matrix and back
into the clean, dry air gas stream after stopping gas exposure 506.
This is manifested as an increase in residual peak area value 2202
during consecutive gas exposures, when sensors are exposed to high
concentrations of gas. This phenomenon was observed at
concentrations above .about.1,000 ppm (the maximum on curve 2202).
At gas concentrations less than .about.1,000 ppm, the time between
exposures was sufficient for gas analytes to desorb from the sensor
prior to the next gas exposure. However, the ultimate sensitivity
in this exemplary data is obscured by the residual adsorbed gas
during exposure of subsequent lower concentrations of gas. For all
gas concentrations, the peak resistance value of the gas response
profile decreased steadily as the gas analyte concentration was
diluted to the lowest concentration recorded, .about.0.05 ppm gas.
Curve 2203 shows the normalized peak area (left vertical axis) for
each consecutive concentration profile vs. gas analyte
concentration, and the log of the normalized peak area 2204 (right
vertical axis) is also plotted vs. gas analyte concentration.
Exposure of sensors to high gas concentrations obscured the sensor
baseline response. In some embodiments of the invention, sensors
may be protected from high gas concentrations, for example by
rapidly expelling adsorbed gas analyte through heating, chemical or
optical desorption methods.
[0147] FIG. 23A-FIG. 23B. FIG. 23A shows an exemplary embodiment of
a region of a gas analyte nanosensor fabricated using the method of
nanoimprint lithography. FIG. 23B shows an enlargement of a section
of the nanosensor having semiconducting metal oxide nanotraces with
a schematic illustration of a diffusion matrix on one nanotrace of
the sensor. Scale bar in FIG. 23A=400 nm.
[0148] FIG. 23A is a scanning electron microscope image of a region
an exemplary nanosensor gas detector useful in embodiments of the
invention. In this example, nanoimprint lithography followed by
nanoimprint lift-off was employed to produce individual tin oxide
nanotraces 2301 that make up the nanosensor. Here nanotraces have a
width of 80 nm and were separated by a pitch of 400 nm (shown as
the white bar connecting the centers of two nanotraces near the top
of FIG. 23A). These dimensions were defined by the nanoimprint
template that was produced by electron beam lithography according
to methods known in the art. Each nanotrace has a high surface area
to volume ratio for the span between consecutive electrodes 2302.
In some embodiments of the invention, electrodes 2302 may be
protected from the environment using masking layer 2303 which
extends beyond the edges of electrodes 2302, while still allowing
for contact of gas analytes with gas sensor nanotraces 2301.
[0149] Because all nanotraces 2301 are connected to the same
electrode 2302, they respond in parallel when exposed to gas
analytes. As such, each sensor 101C represented schematically in
FIG. 1, may comprise 1, 2, 5, 10, 20, 50, 100, 250, 500, 1,000,
5,000, 50,000, 1,000,000 or any number therebetween of parallel
nanotraces, inclusive of the smaller and larger numbers listed,
responding in parallel and enabling the determination of gas sensor
response profiles. In some embodiments of the invention,
illustrated in the enlarged section 2305 of nanosensor 101C shown
in FIG. 23B, a biomolecular diffusion matrix 104C is formed by
anchoring biomolecules 2304 to individual nanotraces 2301 that
collectively impact the overall sensor response profile. In FIG.
23B, for ease of viewing, biomolecular diffusion matrix 104C is
illustrated only on one nanotrace present in the sensor 101C but
could be present on any number of nanotraces in the sensor.
[0150] FIG. 24 shows an exemplary embodiment of a 2.times.2 array
of nanosensors, each sensor comprising semiconductor metal oxide
gas detection material fabricated on CMOS read-out integrated
circuits. Sensor array 102 comprises sensors 101A, 101B, 101C,
101D, each sensor comprising exemplary multiple conductometric
nanotraces 2301 fabricated directly on a silicon CMOS read-out
integrated circuit array. Each sensor 101 connects to individual
circuits in the ROIC through electrode pads 2401 present in
opposite corners of an independently electrically-addressable
sensor 101. ROIC electrode pads 2401 are in contact with electrode
2302, which is connected to multiple conductometric semiconductor
tin oxide nanotraces 2301. The number of sensors in array 102 can
range from 1 to millions, with individual sensors having dimensions
that range from about 100 nm to hundreds of microns. Numerous CMOS
ROIC devices are described in the art. In some embodiments of the
invention, the number of nanotraces and electrode interconnectivity
with sensor array 102 can be coupled to include additional
nanotraces with a larger sensor footprint and more current carrying
capacity, which may improve sensitivity and detection limits.
[0151] In addition to sensors having nanotraces that are 80 nm in
width and set on a 400 nm pitch, as illustrated in FIG. 23A, some
sensors used in experimental examples had thin films or nanotraces
with larger or smaller widths and were set on different pitches.
The total surface area of gas sensor transducer material 2301
present on a given sensor 101 may affect the gas sensor response
profile.
[0152] FIG. 25 shows a series of gas sensor response profiles
determined using linker-derivatized semiconducting nanotrace
sensors having a width of 80 nm and spacing of 400 nm for two
different concentrations of N-methyl-2-pyrrolidone, and a sensor
response profile for one sensor having a nanotrace width of 400 nm
and spacing of 800 nm. The series of 55 replicate gas sensor
response profiles includes response profiles that were determined
with a lower concentration 2501 and a higher concentration 2502 of
gas analyte N-methyl-2-pyrrolidone. Sensor response profiles 2503
for each of 55 gas sensors having a nanotrace width of 80 nm on a
400 nm pitch were similar for lower and higher concentrations of
gas. The plotted sensor response profile 2504 for a single sensor,
having a nanotrace width of 400 nm on an 800 nm pitch showed
response profile characteristics different from those observed for
the set of 55 gas sensor response profiles 2503. (In FIG. 25,
sensor response profile 2504 is represented with a darker line than
the 55 response profiles 2503 and is labeled at multiple
locations.) Sensor response profile 2504 exhibited characteristics
different from those of response profiles 2503 at lower gas analyte
concentration 2501, at higher gas analyte concentration 2502, after
the gas analyte saturated the sensor 2505 (post saturation), and
after a replicate sensor saturation period 2506. These data
indicate that the dimensions and pitch of sensor nanotraces can be
selected and adjusted, just as the diffusion matrix and other
experimental parameters can be varied, to enhance identification
and quantification of individual gas species in a gas sample.
Varying the size and pitch of nanotraces used in combination with
different diffusion matrices can improve the detection and
identification of gas analytes in a test sample.
* * * * *